Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)

1. Breast Cancer Detection from Mammography Images
using Machine Learning Techniques
Mansi Chowkkar
x18134599
Abstract
Breast cancer is the major concern of malignant tumors among all
women in the world. Currently, Mammography is the most common
and widely used screening method for Breast cancer detection. Due
to the complexity of mammograms, it is difficult for doctors to detect
malignant images from the mammogram. Using traditional machine
learning techniques, false negative is still a major concern for cancer
detection. Advanced Deep learning methods for image processing and
breast cancer classification are outperforming traditional methods used
in the medical field. Therefore fully automatic segmentation method
based on a neural network called U-Net will be applied in this project to
find out a region of interest from the mammogram. Finally pre-processed
mammogram will be provided to convolutional neural network-based
DensNet image classifier model. This project will improve the accuracy
and efficiency of mammogram classification using a combination of mass
detection(U-Net) and classification model(DenseNet). The research will
evaluate the efficiency of the model using statistical analysis.
1 Introduction
Cancer is a global disease problem that is spreading rapidly. As mentioned in Tweneboah-
Koduah (2018), International research studied and concluded that a total of 14.1 million
cancer cases newly arrived and 8.2 million people died due to cancer worldwide in 2012 as
compared to any other diseases. Among all cancers which are diagnosed, Breast cancer is
the second most cancer in the world and as a cause of death, it ranks fifth in the world.
Breast cancer is very common in women, it accounts for more than half that is 54%
new cancer cases and 58% death in all developing countries as mentioned in Tweneboah-
Koduah (2018). To reduce death caused by breast cancer invokes early detection and
treatment techniques for breast cancer which includes mammography, MRI, ultrasound,
and breast biopsy methods. The malicious cancer tissues in the breast grow rapidly and
spread towards benign tissues hence early detection is the key solution for the breast
cancer problem. Among all the detection methods, Mammography is the most common,
economical and widely used technique for breast cancer detection.
Reading Mammography image for detecting malignant lesions and masses is a intricate
task for doctors and radiologist because of the complicated structure of the breast. Many
1

2. times often the doctor gets confused within malignant and benign cancer masses due to
its irregular shape and background contrast present in images. In this case doctors get
confused, the second opinion involves more time and cost for the process. Hence digital
image processing and machine learning approaches are being used for the classification
of mammography images. The benign masses from mammography are regular in shape
and malignant masses are in an irregular shape. Hence accurately classifying benign and
malignant masses is a very important in cancer detection. Therefore mass segmentation
is very important step in image pre-processing method before providing images to deep
learning classifier model. For the segmentation, many traditional methods such as local
threshold, global threshold, otsu threshold, and deep learning methods have discussed
in related work. Due to noise and background artifacts present in mammography im-
ages, traditional methods are not efficient for precise segmentation. Increased data and
deep learning techniques have made progress in the medical imaging field. Convolutional
neural network (CNN) can remove non-linearity from images and enhances the segment-
ation process. Although CNN is an advanced method for segmentation, it gives limited
accuracy due to repeated convolution calculations. This research will be using the U-Net
segmentation method based on a fully convolutional network method as mentioned in Li
and Dong (2019). This method concatenates the shallow and deep layer output hence
provide better output as compared to CNN.
Even though machine learning techniques serve the purpose of predicting cancer from
mammography image classification, sometimes it may produce false negative and false
positive results. To overcome these problems patient has to undergo biopsy process which
is an invasive method which may produce unnecessary stress, anxiety, and health care
cost. Hence this paper introduces deep learning based deeper DenseNet neural network
model as studied in Li et al. (2019). The architecture suggested here using different layers
in DenseBlocks is a novelty in large mammography image data classification. This model
increase the efficiency of the breast cancer detection process increases accuracy and re-
duces computation time. DenseNet model prevents over-fitting problems with small data
set due to image enhancement techniques in the design as explained in the methodology
section. This model will add value to clinicians and radiologists for breast cancer de-
tection from mammography images efficiently. It will provide diagnostic capabilities to
the radiologist in less amount of time. This will also allow patients to take the second
opinion confidently and in less cost.
1.1 Background and Motivation
Using machine learning, classifying breast mass is the key to the diagnosis. In this detec-
tion process segmentation of breast, mass plays an important role. Because of the diverse
features of breast masses, accurate segmentation of mass is a hot issue. As mentioned
in study Sonar and Udhav (2017), grey level covariance matrix is used for segmenta-
tion(GLCM) and in Hussain et al. (2018) study, region growing threshold method(RGT)
is mentioned for segmentation. In all traditional methods, there is a necessity to set
parameters for image extraction. In a mammogram, there is a lot of noise and artifacts
present, due to this background becomes complex and finding a small size of mass using
2

3. traditional segmentation is not ideal. Advanced research in deep learning in medical field
motivated to use CNN technique in mammogram segmentation. CNN read input and
output mapping and automatically extracts features from the image. CNN uses image
blocks around pixel which causes storage problems and affects accuracy. To solve this
problem in Zhang et al. (2018) proposed FCN based image segmentation method. This
method replaces all layers which are fully connected to CNN. Mammogram requires more
precise segmentation hence we will be implementing the U-Net segmentation method in
this research project. U-net, provide high-resolution information by connecting shallow
and deep layer feature extraction by using skip layers in between.
Deep learning methods for the identification of malignant and benign breast cancer
are important to research in breast cancer diagnosis and treatment. CNN method can
accept 2D as well as 3D images as input and avoid complex feature extraction process.
CNN uses local connection hence it reduces the number of parameters and complexity
of the computation as compared to FCN. In this research, we will use the DenseNet
CNN model for mammogram classification as it has advantages over the traditional CNN
model as explained in Li et al. (2019). DenseNet reuses features by connecting to its
previous layers and uses a small number of feature extraction which reduces redundancy
in the network. As mentioned in Yan et al. (2019), DenseNet is first invented by Huang
in 2017. It consists of a novel DenseBlock structure, in which all layers can be directly
connected. This project will be combining the U-Net segmentation method with the
DenseNet classifier model which is a novelty in mammogram mass classification in breast
cancer detection.
1.2 Research Question
”How efficiently, accurately, and quickly machine learning algorithms detect
malignant breast cancer from mammography image data”
1.3 Objectives
• Access images from open source (Kaggle) using python programming language.
• Remove unwanted noise, borders, non-linearity from images using image processing.
• Find the region of interest(ROI) by using an automated U-Net segmentation method.
• Output of the automated U-Net model, provide to a DenseNet classifier model for
identifying malignant and benign tumor from the mammogram.
• Validate the accuracy, sensitivity, specificity, and efficiency of our model using stat-
istical methods.
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4. 2 Related Work
2.1 Machine Learning in Health care
Machine learning has become a popular technology in the industry. It is playing an im-
portant role in Medical science, finance, and security. Machine learning helps to find
out different patterns from medical data and provide techniques to predict diseases ac-
curately. In Shailaja et al. (2018), machine learning importance in medical applications
is explained. Many people die every year due to errors in the health care system. An
efficient system based on machine learning technology benefits doctors to take the right
decisions. Doctors and machine search for patterns involved in disease data. A doctor
cannot evaluate every disease of every patient but the machine can perform all tasks
and provide an outcome result to the doctor for each disease. Machine learning shows a
crucial role in health care including image segmentation, image registration, image an-
notation, image database retrieval, where failure is untreatable. Machine learning reduces
the health care price, increases accuracy and serve as a communication medium between
patient and radiologist. Most of the data in health care are unstructured data that
contains images, videos, recordings, discharge summary, and audios. Machine learning
algorithms are useful in the identification of complex patterns in medical data especially
in clinical applications such as genomics and proteomics.
Currently, radiologists analyze the medical images where there is a limitation on the
number of expert and experienced radiologist as well as speed for the analysis. There-
fore, an automated machine learning algorithm is required in the health care industry.
As mentioned in Ker et al. (2018), MYCIN system implementation in medicine was the
earliest technique that suggested different antibiotic therapies for patients. Artificial
Intelligence(AI) algorithms also moved forward from manual to feature extraction tech-
niques and supervised learning algorithms. AI is a vast domain where Machine learning
is a subpart of AI. Unsupervised technology has also been tested but from 2015-2017 re-
searchers have preferred supervised learning over unsupervised methods in their research.
Advantage of machine learning technique such as a deep neural network is its capabil-
ity to automatically combine low-level features from images such as lines or edges into
shapes. A study done by Ker et al. (2018), Convolutional Neural Networks (CNN) is
best suited for two dimensional as well as to three dimensional organ images to identify-
ing normal versus abnormal disease. In a nutshell, the machine learning involvement in
health care allows radiologist, doctors, and patients to take second opinion which provides
satisfaction and create confidence among patients.
2.2 Mammography in Breast Cancer Detection
Early detection of breast cancer is the only way to reduce the risk of death rate signi-
ficantly. Mammography is the most common method of breast cancer detection. Breast
cancer can be represented as a microcalcification, mass, asymmetry, or distortion present
4

5. in mammography. In mammography breast is exposed to the x-ray radiation. Absorption
of x-ray radiations for normal tissue and calcified tissue is different. This difference needs
to be accurately imaged and further detected in the analysis. Tomosynthesis is a new
mammography technique approved in 2011. In this technique, multiple x-ray images are
taken in different angels and then reconstruct them into a video. Overlapping of dense
tissues from the breast and tumor mass is not present in Tomosynthesis and hence it
helps radiologists to find abnormalities from the breast. Many studies have been done
on different techniques in mammography. Gupta et al. (2018) studied high resolution
ultrasound(US) technique for characterizing and detecting lesions present in breast. Ul-
trasonography(USG) is useful in suppressing tumor modality to x-ray mammography.
Its main role is to differentiate between simple and complex tumor growth. Recently
x-ray mammography and ultrasound images are combined for breast lesion characteriz-
ation. The survival rate for an early detected breast tumor is up to 99 percent while if
it spreads to other organs then the survival rate drops to 24 percent. High resolution
ultrasound technique is effective for microcalcification detection from the breast. The
study from Gupta et al. (2018) shows that when findings of Digital Mammography(DM)
is combined with USG, it differentiates malignant and benign lesions accurately. This
case study concludes that mammography is important in breast cancer detection. The
high resolution US in a specific case is used where breasts are dense to provide a bet-
ter solution for differentiating between simple and complex lesion. This is cost-effective,
highly efficient, and quick imaging technique for breast cancer detection.
Digital Breast Tomosynthesis(DBT) is the new technology for breast sceening which
reduces the overlapping tissue effect. In Mohindra et al. (2018) DBT results compared
with DM to help in better characterizations of abnormalities in mammography. The
DBT, x-ray tube moves during a test in an arc to acquire two-dimensional projection.
These projected images then reconstructed to form thin image slices of about 1 mm
thickness which helps to find subtle abnormalities in the breast. The limitations on DM
is an overlapping of tissues in the case of dense breast mammography. Studies have shown
that DBT shows a reduction of overlapping of tissues in mammography screening. As
per this study, DBT shows superior cancer visibility as compared to DM. According to
Mohindra et al. (2018), DBT is a useful technique in breast cancer detection in the case
of dense breasts as compared to fatty breasts. In Wang et al. (2016) study, single view
and double view fused feature model is is proposed. This fused method is arranged by
feature selection methods. In this study Computer aided diagnosis(CAD) method based
on machine learning is also proposed for improving breast cancer detection. Currently
CAD method provide radiologists better accuracy and efficient results in breast cancer
detection by examining only medio-lateral oblique(MLO) and cranio-caudal(CC) view of
the mammography image. Proposed fused method of single view feature with contrasting
double view feature is helpful to radiologist to simulate breast cancer diagnosis process.
Machine learning based CAD method with combined feature improves the effectiveness
of breast cancer detection method.
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6. 2.3 Image Processing using Machine Learning
Medical segmentation imaging is growing rapidly as a research area in medicine for early
treatments. Digital image processing is important to retrieve useful characteristics from
images. Before the image is provided as an input to ML or DL it has to undergo many
processes for example: image has to divide into many segments, the feature is extrac-
ted, and then the noise is removed from the image. Image processing is required for the
medical imaging field for its success in disease prediction. The digital image processing
provides accuracy and feature extraction in the medical field. The deep learning, machine
learning, and artificial learning play an important role in medical image processing such
as image classification, image segmentation, and pattern extraction process as studied by
Latif and s. Tu (2019). The pixel analysis is one of the image processing technique which
is better than feature extraction as per the research proposed in Latif and s. Tu (2019).
This method works better with images having low contrast which is a challenge in the
image processing field. The models with many layers adapt input images and give output
for disease accurately. This type of model is the Convolutional neural network(CNN).
The deep learning classifies medical images which are the main task to investigate the
disease. Here, the author used three CNN methods for image classification and the result
is presented in a matrix. In the medical imaging field, deep learning classifies, itemize,
categorize the images using image processing techniques. In Varma and Sawant (2018)
research proposed texture segmentation processing on mammography images to examine
the initial stage of the tumor. This method differentiates and classifies malignant micro-
calcifications and other cancerous areas. In texture based classification method not only
the image but also objective plays an important role. Though this texture analysis is
limited to the detection of breast cancer, it can also be applied to any biomedical images
with selecting an appropriate threshold. Using segmentation, the threshold coupled with
texture analysis achieve high quality detection, efficient feature extraction and visualiza-
tion. The reduced processing speed and time required by this technique is an advantage
of this method.
The segmentation method for medical images varies as per the application like heart
segmentation is different from the brain segmentation method. In Merjulah and Chandra
(2017), different segmentation methods have compared to address the best method. Ac-
cording to this research, CNN performs best in providing a solution for medical prob-
lems. CNN has the ability for segmentation, image classification, and automatic detection
hence it shows impressive performance in the field of image processing in medical. Though
mammography is the most common and effective method of breast cancer detection, radi-
ologists required CAD methods to assist them in their overloaded work. In Yeh and Chan
(2018) this study, machine learning techniques have been proposed for decomposition and
recompilation of mammograms for detecting abnormalities in mammogram image data.
The decomposition is used to expand mammogram dimensionality which consists of a
multiband generation process(MBGP). Recompilation generates different mammography
views by using principal component analysis(PCA). Using these methods abnormalities
in mammography can be easily detected. In this research first images preprocessed to
remove pectoral muscle region and artifacts. Multiband images generated using dimen-
sion expansion and then different mammography views generated using recomposition.
Finally abnormalities from mammographic images identified by using the segmentation
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7. method.
All the explained segmentation methods are not able to semantically label the im-
ages. In this deep learning era, for pixel level segmentation CNN and fully convolutional
neural(FCN) based segmentation methods are evolving. Currently CNN is the most com-
mon deep learning method for image classification. Image segmentation is a common in
medical as well as natural image processing. In Weng et al. (2019), FCN based and U-Net
inspired architecture is implemented for image segmentation in medical imaging field. In
U-Net architecture, up-sampling, downsampling layers and max pooling, average pool-
ing is used to increase the resolution of the image. FCN based segmentation improves
resolution of the image for better classification results.
2.4 Automated mammography reading using Machine learning
technique
As we have seen mammography is most widely used screening for breast cancer detec-
tion hence, a massive number of mammograms are generated everyday in the hospital.
To predict the result radiologist required automated and accurate digital mammogram
reading machine learning technique (MLT). In Yassin et al. (2018), many MLT for breast
cancer detection from different modalities have been compared. According to study, it is
seen that SVM is used largely in many research studies for breast cancer classifier. Arti-
ficial intelligence and deep learning methods have an effective classification and detection
methods in the medical field hence its implementation is increasing. The CAD techniques
consist of some pitfalls such as it increases the time and cost due to false positive results.
Hence MLT is developing to reduce false positive results and promising deep learning
classifiers are also appearing in recent years.
2.4.1 Support Vector Machine(SVM),K-Nearest-Neighbor(KNN), Linear Re-
gression(LR)
In the research Sonar and Udhav (2017), hybrid SVM-KNN machine learning classifica-
tion method based on a mammogram is proposed. Region of interest(ROI) is extracted
using segmentation and active contour method. For the texture feature extraction grey
level covariance(GLCM) method is used. This feature vector is provided as an input to
the modified SVM-KNN classifier and the result is compared with SVM, KNN, and ran-
dom forest. The complexity of the features proposed for the classifier model can reduce
in the future by using feature optimization is concluded by the author. The mixed grav-
itational search algorithm (MGSA) and SVM are implemented by Shirazi and Rashedi
(2016) to detect breast cancer from mammography images. For the segmentation sech
template matching method is used for ROI extraction. For feature extraction, gray level
co-occurrence matrix(GLCM) is used.
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8. 2.4.2 Random Forest(RF), Decision Tree
Hussain et al. (2018) study uses dual energy x-ray absorption(DXA) images data for
presenting a new method called Pixel Label Decision Tree(PLDT) to check performance
in the femur segmentation method. The performance of PLDT is measured with global
threshold, artificial neural network, and region growing threshold image segmentation
methods. The optimal supervised feature selection is the limitation of this method which
can be resolved using a deep learning segmentation method. The Random Forest(RF)
classifier is proposed in Sunitha and Shruthi (2018), which shows image processing, seg-
mentation, feature extraction, and classifier process to compare the result with SVM.
Mammography images provided as input after removing noise, segmentation and image
enhancement. The extracted feature from the segmentation method using GLCM, a re-
gion of interest(ROI) is obtained which is used as test data. RF compares test data with
trained data and provides textural feature extraction. Result in this study shows RF
achieves more accuracy as compare to SVM.
In medical imaging field classifying imbalanced data is a challenging task. For example
in breast cancer detection, images with cancerous mass are more than non cancerous
that shows data is skewed distributed which is called imbalanced data. The imbalanced
data produces a false result as classifier are biasing towards majority data compared to
minority data. Paing and Pintavirooj (2018) research proposes an ensemble approach
of RF method which is a combination of multiple decision trees. This study proposed
different sampling methods and compared the performance of RF. Results show that
RF with sampling method performs better than RF without sampling. In future, more
advanced sampling methods can be applied for achieving better performance. Among all
sampling methods, Systematic Minority Oversampling Technique performed well as per
the study.
2.4.3 Deep Learning: Convolutional Neural Network(CNN), Deep Neural
Network(DNN), Back Propagation Neural Network(BPNN)
In Nahid and Kong (2017) research study, the CNN classifier method is explained for
breast cancer images. This technique uses kernels for global feature extraction and these
features are being used in image classification. The texture feature extraction method
is important method since it represents low level information which gives all detailed
information about an image. This study shows that most of the image classifiers de-
pends on the feature extraction process. Due to global feature extraction property, CNN
can extract hidden information from the image. The difficulty in current deep learning
methods is the complexity present in terms of computation and mathematical algorithms
involved. In this study author also focused on the light deep neural learning model(DNN)
to reduce timing complexity and computation complexity. DNN can be combined with
any other learning techniques in the future to provide a more positive result. In Dhun-
gel et al. (2017) research, the author presented an integrated method for breast cancer
mass detection, segmentation, and classification with less user involvement. For finding a
solution, the problem is broken into three stages: For mass segmentation deep structured
8

9. learning method is proposed which is refined by the level set method. For classification
deep learning classifier is proposed. This classifier is trained using regression to feature
values and tuned by annotation values. For detection, the cascade of random forest and
deep learning method is proposed. This study believes that this setup can be used in
the clinic as a second reader for the radiologist. In this study, U-Net segmentation is
proposed as a future work for an achieving better efficiency and accuracy.
Azli and Samad (2017) investigates the mas classification from a mammogram us-
ing a back-propagation neural network(BPNN) classifier with different types of features.
Using linear normalization method images are preprocessed. Using otsu thresholding
segmentation method features are extracted and provided as an input to the classifier.
Layers used in BPNN are input, output, and hidden. This study shows that different
sets of features influence the classifier performance hence extracting proper features for
differentiating between malignant and benign class is important. It also proved that with
100 hidden nodes performance increased by 10 percent as compare to 3 hidden nodes in
the classifier. The proposed architecture can further be improved to acieve neural net-
work performance. The research done by Bandeira Diniz et al. (2018) presents automatic
mass region detection method using CNN. This method consists of the testing phase and
training phase. The image preprocessing, segmentation, registration, first false positive
reduction, segmented region preprocessing, density tissue classification, and secondary
reduction of false positive are steps implied for the testing phase. For the testing phase,
model is classified for dense, non dense breast tissues and mass, non mass regions. The
result shown that model achieved 95.6 percent accuracy and 90.4 % sensitivity.
As the all mentioned deep learning studies show that CNN is commonly used and
provides efficient results in the medical field for image classifier models. Efficiency and
accuracy can be increased more by using a shorter connection between CNN layers. Huang
et al. (2017) introduced DenseNet convolutional network which connects each previous
layer. DenseNet allows the re-usability of features and it is a more accurate and compact
model. It is a good feature extractor and integrates all properties of deep supervision and
deep learning. As compare to GoogLeNet, VGGNet, and AlexNet DenseNet is showing
better accuracy, sensitivity, specificity, and classification performance.
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10. Reference Preprocessing
Method
ML method Data Set Accuracy Future Work
and Gaps
Wang et al. (2016) Median fil-
ter and en-
hancement of
contrast
Extreme
learning ma-
chine(ELM)
222 digital
mammo-
graphy
images
95 % genetic al-
gorithm
selection
Sonar and Udhav
(2017)
grey level
co variance
matrix(GLCM)
Hybrid
KNN-SVM
MIAS 95 % Reduce num-
ber of feature
for training
Sunitha and Shruthi
(2018)
GLCM Random
Forest(RF)
250 Mam-
mography
images
Higher
than SVM
NA
Dhungel et al.
(2017)
Discrete Wave-
let transforma-
tion(DWT)
BPNN 209 Mam-
mography
images
93 % Use FCN or
U-Net for
segmentation
Azli and Samad
(2017)
otsu threshold-
ing
BPNN 40 MIAS 10 % more
accuracy in
100 nodes
model than
3 nodes
NA
Bandeira Diniz et al.
(2018)
CNN CNN density
classification
250 Digital
database
screening
mammo-
graphy
(DDSM)
97 % decrease
number of
parameters
in CNN
Paing and
Pintavirooj (2018)
SMOTE, Ran-
dom Over-
sampling,
Random Under
sampling
RF UCI Medical
data
with
sampling
perform-
ance is
good
Use of more
image data
and modern
sampling
Hussain et al. (2018) Region growing
threshold(RGT)
PLDT 600 DXA im-
ages
91.4 % NA
Shirazi and Rashedi
(2016)
sech template
matching
MGSA-SVM MIAS MGSA-
93.1 %,
SVM-86 %
NA
Weng et al. (2019) U-Net, NAS-
UNet
NA ultrasound
nerve images
NAS-UNet
outper-
forms
NA
Huang et al. (2017) NA DenseNet street view
house images
DenseNet
with L=
190 nad k=
40 outper-
forms
use of feature
transfer with
DenseNet
Table 1: Machine Learning Models Overview
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11. 3 Methodology
This research work studies how DenseNet based on FNN model and U-Net segmentation
can be applied on Mammography image data classification for breast cancer detection.
The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology will
be applied in this research project. Business understanding, data understanding, data
preparation, and, modelling have explained in this section. Evaluation and deployment
will be discussing in section5.
3.1 Business Understanding
Radiologists and doctors required more time for analyzing mammography images for
Cancer detection. Breast cancer detection require many trained and knowledgeable radi-
ologist as a large number of mammogram gets generated worldwide in a single day. Deep
learning techniques in image processing and classification method will increase efficiency,
accuracy and reduces the time required for analysis. CNN based segmentation and clas-
sification will reduce the time required for analysis and provide different objectives to
medical science. This research based on deep learning segmentation process and CNN
based image classification model will provide a solution to a radiologist for detecting
breast cancer more efficiently and accurately in less amount of time.
3.2 Data Understanding
In this research will be using mammography image data from kaggle which is publicly
available. The data has been uploaded recently and it is version 1 data with 5000 images
This data is originally from MIAS database as mentioned in Azli and Samad (2017).
Each image is having a size of 1024 * 1024 pixel with a digitized edge at 50-micron pixel.
The data provide detailed information about dense and fatty breast as well as a benign
and malignant tumor. The data contains calcification, asymmetry, distorted, and well-
defined image classes. Considering all these factors about image data will discuss all data
preparation steps in the following section.
3.3 Data Preparation
A Mammogram is a high resolution and low radiation image hence it is difficult to in-
vestigate the image therefore, image preprocessing is required as mentioned in Li et al.
(2019). Preprocessing improves the accuracy, quality, and quantity of the image. It con-
sists of noise removal, segmentation, extraction of a region of interest, and augmentation
methods.
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12. 3.3.1 Image Pre-processing
Noise and Artifact Removal: In a mammogram, there may be a presence of noise such
as straight lines and curvilinear lines. For removing noise from the image, we will be
using a median filter method using 3 * 3 neighborhood window by not affecting edges
of the image as mentioned in Omair (1987). This method updates a subset of windows
instead of updating the histogram, therefore will achieve independent execution time. For
finding the median algorithm make use of one adjacent left window value and one previous
window just above level value. Artifacts will be removed using the global thresholding
method as mentioned in Anitha and Peter (2015). Background and Edge removal: For
removing background, images are divided into low intensity and high-intensity groups.
This process will convert the color image into grayscale image such as low-intensity group
will be replaced by 0 and high-intensity group will be replaced by 1. Edges will be removed
from image pixels using the normalization method. In normalization pixels of the images
are placed between 0 and 1 range. It is expressed as: zi =
xi − min(x)
max(x) − min(x)
In this equation x is the image from mammography data from kaggle and zi is the
normalized image.
3.3.2 Segmentation and Augmentation of images
Image segmentation is the process of the partitioning of the image into various segments,
locate edges, boundaries from the image, and label each pixel from a digital image. Each
pixel present in the same region show similarities like color, texture, and intensity. The
segmentation will help radiologist to easily distinguish between regular shape benign and
irregular shape malignant tumors from mammogram. As we studied from literature,
Dhungel et al. (2017) has introduced the U-Net segmentation method for future work to
improve the performance of the CNN classifier, will be using this segmentation in this
research with CNN based classifier is an innovation for our study. As implemented in Li
and Dong (2019), Fully Convolutional Network(FCN) based U-Net architecture will be
implemented here.
As shown in the figure 1, U-Net consists of encoder and decoder. An encoder uses
an architecture of CNN and acts as a feature extractor for the network and performs
downsampling. The down sampling includes 3*3 without padding convolutional layers
and 2*2 maximum pool layer. For each sampling, feature channels are doubled and feature
map size is reduced to half. The upsampling is handled by the decoder in this structure.
Each of the up - sampling consists of a 2*2 de-convolutional layer and 3*3 without
padding convolutional layers. The shallow layer created feature maps is then concatenated
with the output of the deep layer by skipping the connections as we have shown in the
figure1. In up sampling, image size is doubled and feature channels are reduced by
half. For concatenation of deep layer output and shallow layer output, Rectifier Linear
Unit(ReLU) function is used hence network simultaneously considers both shallow and
deep information. This will provide the best segmentation output in the medical imaging
12

13. Figure 1: U-Net Architecture
field. In the Neural network, large data is required for training and testing. Using
Augmentation we can increase our data by rotation and flip activities. This will improve
our model during training.
3.4 Modelling
Related work has shown that deep learning overcomes with limitation that are present
with traditional machine learning techniques such as manually designing and extracting
features. As studied in related work, CNN is the most commonly used and can be directly
use on 2D and 3D image data. As compare to fully connected neural network CNN
contains downsampling, use local connection, and use less network parameters which
reduces computational complexity. CNN can be more accurate, deeper and efficient if it
contains shorter connections in between output and input layers as mentioned in Huang
et al. (2017). LeNet5 consist of 5 layers, ResNet can consist 100 layers, and VGG 19. All
these network models consist of different network topology but they contains same key
characteristic which create short path from input layer to output layer.
In our design will be following three tier architecture as studied in Tie and Jin (2011).
The idea behind this architecture is to distribute data storage, data accessing, image
pre-processing, segmentation method, image classification and final output. Data tier
will explain from where and how we are going to access data. Logic tier will consist of
out normalization, segmentation and image classification logic. Business tier will show
how radiologist or doctor will see the output of image classification.
This paper discusses characteristics and merits of three-tiered architecture in the
13

14. Figure 2: Three tier design
system application, points out the core idea of three-tiered architecture is the distribution
of calculation and data. This Paper put forward a basic principle that the middle tier
should be a buffer of other tiers and the balance point of all loads, and it should be
expanded for future business requirements. Based on this principle, we propose a new
perspective that the middle tier in traditional three-tiered architecture should be logically
divided into Basic Business Tier, Kernel Business Tier and Business Presentation Tier.
Finally, the realization of this division is introduced with the example of the doctor’s
advice management in Hospital Management Information System
In this research we are using densely connected DenseNet neural network architecture.
Using U-Net segemented images as an input to DenseNet neural network is a novel idea
will be implementing here for malignant and benign classification. Each layer in this
model is connected to the previous layer for re usability of features. Due this kernel
extraction use by each layer is very small which reduces redundancy. In DenseNet , all
the layers in the channel dimension are interconnected with previous layers. Let consider
an example where L layers are present in network, total connections in the DenseNet can
be presented by formula : DenseNet =
L(L + 1)
2
As we discussed DenseNet is reusing features, strenghening feature propagation, solv-
ing gradient disappearance problem effectively, and reducing number of parameters sig-
nificantly as mentioned in Li et al. (2019). This model does not require pre-training of
images hence it is a time saving model.
3.4.1 Design
DenseNet consists of DenseBlock and Transition layers. To avoid wider network, the total
40 layers of DenseNet network with the growth rate = 12, 2 transition layers, and 3 k
14

15. DenseBlock layers is designed. As we can see in the figure(3), this model consist of very
few parameter hence it saves computational memory and avoid overfitting.
DenseBlock layer:
For a single image is passed through a convolutional network. L number of layers net-
work implements non lx0inear transformation function Hl(.). In this case H(.) indicates
composite functions such as rectified linear units(ReLU), batch normalisation(BN), con-
volution(Conv), or pooling. In denseblock each layers output after convolution operation
is converted to k-characteristic map. The feature map for the each layer in denseblock
is having same size as k. This implies total k number of kernels are used for feature
extraction. Lth layer with k0 channels can be represented as DenseNet = k0 + k∗l − 1
input feature-maps in the input layer. k indicates the growth rate hyperparameter in
the DenseNet. DenseNet internally uses bottleneck layer so that computation time can
be reduced
computationtime = BN + ReLU + 1∗lConv + BN + ReLU + 3∗3ConvStructure
As shown in fig(3) 1 ∗ 1Conv reduces number of features to improve efficiency and
calculation speed.
Figure 3: DenseNet Architecture
Transition layer:
This layer connects two dense block layers which are adjacent to each other so that
feature map size is reduced. The Transition layer consists of a 2 ∗ 2AvgPooling and and
1 ∗ 1 convolution operations. The structure can be represented as:
transitionlayer = BN + ReLU + 1∗lConv + 2∗2AvgPooling
The Transition layer can be defined as a function of a compression model. Each
15

16. transition layer and previous layers will connect as input:
xl = Hl([x0, x1, . . . , xl−1]) (1)
H1(ffl) is defining non-linear function for transformation of ReLU, BN, Conv, and
pooling operations. The last dense block sent to the image recognition softmax classifier.
As a novelty, in our model will make changes in model output to get vector c of images
and will classify it with sigmoid function. The second change is, the modify loss function
will be modified to unweighted cross-entropy as explained in the following formula:
L(X, y) =
7
c=1
[−yc log p(Yc = 1|X)
− (1 − yc) log p(Yc = 0|X)] (2)
Yan et al. (2019)
4 Implementation
The segmentation part will be implemented in the python programming language. We
will be using Keras interface of DenseNet pre-trained model. For image data will be
using the cloud as a storage and python API for accessing images. For calculating the
difference between real and predicted value cross entropy loss function will be used. Using
back propagation method, data loss will be transmitted to the network layers which will
update each layer of the network with biases and weights. The output produced by U-Net
segmentation is then provided to a DenseNet classifier model using 3 dense blocks with
an equal number of layers. Before entering to dense block 1, convolution layers of 56
output Chanel is performed( generally twice the growth rate value is considered). The
following table provides more information about L value and k value for the DenseBlock
layer and Transition layer.
16

17. Layers Output DenseNet
k=12
DenseNet
k=24
Convolution 56 *56 7 * 7 Conv 7*7 Conv
Pooling 56 *56 3*3 max pooling 3*3 max pooling
Dense Block 1 28*28
1 ∗ 1conv
3 ∗ 3conv
*3
1 ∗ 1conv
3 ∗ 3conv
*3
Transition 1 56*56
28*28
1*1 conv
3*3 max pool
1*1 conv
3*3 max pool
Dense Block 2 14*14
1 ∗ 1conv
3 ∗ 3conv
*6
1 ∗ 1conv
3 ∗ 3conv
*6
Transition 2 28*28
14*14
1*1 conv
3*3 max pool
1*1 conv
3*3 max pool
Dense Block 3 7*7
1 ∗ 1conv
3 ∗ 3conv
*12
1 ∗ 1conv
3 ∗ 3conv
*12
Classification
layer
7*7 global avg
pool
7*7 global avg
pool
7*7 global avg
pool
Table 2: DenseNet Implementation structure
5 Evaluation and Deployment
This research study will evaluate malignant and benign mass classification. The perform-
ance of the DenseNet model will be validated from specificity, accuracy, and sensitivity
calculations. The confusion matrix will be used to calculate sensitivity, accuracy, and
specificity. Sensitivity is the ratio of correctly identified positives values and Specificity
is the ratio of correctly identified negatives.
Sensitivity(Sen) =
TP
TP + FN
Specificity(Spec) =
TN
TN + FP
Accuracy(Acc) =
TP + TP
TP + TN + FP + FN
In the above formula, TP(true positive)represents breast images with malignant tu-
mors which model identified accurately. FP(false positive) represents benign breast can-
cer images which our model mistakenly identified as a malignant one. TN (true negat-
ive)represent breast images with benign tumors which model identified accurately. FN
(false negative) represents benign breast cancer images which our model mistakenly iden-
17

18. tified as a malignant one. The TP and FP can further introduce a receiver operating
characteristic (ROC). ROC curve will be plotted as a two dimensional curve. The ROC
curve area that is AUC value in the [0,1] interval and x axis provides overall performance
of the model. The greater the AUC value, the better is the performance of the model.
For evaluating how closely data matched with model output and accuracy of malignant
breast cancer by machine learning model Kappa statistic will be used.
Kappa =
ObsercedAccuracy − ExpectedAccuracy
1 − ExpectedAccuracy
This research implementation will be done on the intel i7 8th generation Lenovo
machine with 8GB ram and 256 SSD. One the research is completed, then this model
can be deployed to any clinic or hospitals for breast cancer detection from mammogram
images.
6 Project Plan and Ethics
Figure 4: Project Plan Phase 1
Figure 5: Project Plan Phase 2
Figure 6: Project Plan Phase 3
In this research study, ethics is the most important factor to be taken into account
while selecting data. Data will be extracted from kaggle which is a public data set.
18

19. 7 Conclusion
This research project provides a quick and efficient methodology for breast cancer de-
tection problem from mammography images using deep learning approach. The research
studies deep learning methods based on CNN and FCN for image processing and clas-
sification for breast cancer detection. Firstly aiming to remove noise, borders and non-
linearity from mammogram using normalize image processing technique. FCN based
U-Net segmentation model will be implemented for automated feature extraction from
the mammogram. This extracted image is classified as a malignant and benign tumor
using a DenseNet classifier. To overcome performance and accuracy issues from the
traditional CNN classifier model a DenseNet model will be implemented with few modi-
fications to achieve good efficiency, accuracy, and sensitivity. This model will accurately
classify malignant and benign breast cancer from mammogram image data which adds
value to breast cancer diagnosis and treatment in health care. This research will reduce
breast cancer death rate in the world and reduces health care cost by avoiding invasive
biopsy method of cancer detection.
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