This document discusses an automated system for detecting and classifying meningioma tumors from MR images using optimized deep learning models. The research aims to (1) accurately detect meningioma tumors through a fully automatic segmentation procedure and determine tumor grade and location, and (2) precisely and automatically classify and identify tumor grades with minimal expert input using deep learning algorithms. The proposed system includes segmentation using K-means clustering and a convolutional neural network with a hybrid harmony search algorithm, followed by tumor grade identification with a deep learning model.

1. Automated Detection and classification of Meningioma Tumor
from MR Images using optimized Deep learning Models
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Aneesh S Perumprath
Dr. K.Martin Sagayam (Guide)

2. INTRODUCTION
 Among brain tumors, menignomas are the most common and aggressive,
leading to a very short life expectancy in their highest grade. Thus,
treatment planning is a key stage to improve the quality of life of
oncological patients.
 Magnetic resonance imaging (MRI) is a widely used imaging technique
to assess these tumors, but the large amount of data produced by MRI
prevents manual identification in a reasonable time, limiting the use of
precise quantitative measurements in the clinical practice. So, automatic
and reliable segmentation and classification methods are required;
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3. REASERCH FOCUSSING (SCOPE)
 For better treatment & its planning ,my research is mainly focused on:
 (a) an accurate detection procedure of Menignoma tumor’s with the help of a
fully automatic state of art segmentation procedure to determine tumour grade
and location.
 (b) A precise & automatic classification and grade identification of the detected
tumor with minimum labour for the clinical expert using deep learning
algorithm.
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4. Problem Statement
 In brain MRI, segmentation is a mandatory task which can be done manually by an
expert with good accuracy but time-consuming. At the same time, fully accurate and
automatic segmentation approaches are not yet authentic. Currently, clustering based
methods can be effectively applied to brain image segmentation, there are two main
problems to be solved which are:
 The sensitivity to noise and intensity inhomogeneity artifact
 The trapping into local minima and dependency on initial clustering
centroids.
For the purpose of obtaining satisfactory segmentation performance and dealing
with the problems mentioned above, an effective segmentation approach need to be
developed within the scope of this research.
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5. Problem Statement
 Moreover, presently various automated approaches are presented for brain
tumors detection and type classification.
 In most of these approaches Support Vector Machine (SVM) and Neural
Networks (NN) are the widely used for their good performance over the last few
decades.
 But recently, deep learning (DL) models set an exciting trend in machine
learning as the DL architecture can efficiently represent complex relationships
without requiring a huge number of nodes like in the shallow architectures.
 For the above reasons, in this research we plan to develop an automatic brain
tumor grade identification with the help of effective segmentation along with
Deep learning model
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6. BLOCK DIAGRAM
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Segmentation
Kmeans clustering
CNN with HHO

7. SUBJECTS
 14EC3030 BIOLOGICAL SIGNAL PROCESSING.
 14EC3031 MEDICAL IMAGE PROCESSING.
 14EC3034COMPUTER BASED MEDICAL INSTRUMENTATION.
 14EC3026 TESTING AND TESTABILITY.
 14EC3003 COMPUTATIONAL INTELLIGENCE AND OPTIMIZATION TECHNIQUES
 ece-2019%20(1).pdf
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8. DISCUSSIONS
&
THANKS
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