Enviar pesquisa
Carregar
Early detection of adult valve disease mitral stenosis
•
0 gostou
•
274 visualizações
IAEME Publication
Seguir
Denunciar
Compartilhar
Denunciar
Compartilhar
1 de 10
Baixar agora
Baixar para ler offline
Recomendados
Analysis of Neocognitron of Neural Network Method in the String Recognition
Analysis of Neocognitron of Neural Network Method in the String Recognition
IDES Editor
Af4102237242
Af4102237242
IJERA Editor
Auto Configuring Artificial Neural Paper Presentation
Auto Configuring Artificial Neural Paper Presentation
guestac67362
VLSI IN NEURAL NETWORKS
VLSI IN NEURAL NETWORKS
Mohan Moki
Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Al...
Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Al...
IJECEIAES
Differential Protection of Generator by Using Neural Network, Fuzzy Neural an...
Differential Protection of Generator by Using Neural Network, Fuzzy Neural an...
Waqas Tariq
Neural Network Applications In Machining: A Review
Neural Network Applications In Machining: A Review
Ashish Khetan
Jack
Jack
Ericsson India Global Service Pvt Ltd
Recomendados
Analysis of Neocognitron of Neural Network Method in the String Recognition
Analysis of Neocognitron of Neural Network Method in the String Recognition
IDES Editor
Af4102237242
Af4102237242
IJERA Editor
Auto Configuring Artificial Neural Paper Presentation
Auto Configuring Artificial Neural Paper Presentation
guestac67362
VLSI IN NEURAL NETWORKS
VLSI IN NEURAL NETWORKS
Mohan Moki
Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Al...
Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Al...
IJECEIAES
Differential Protection of Generator by Using Neural Network, Fuzzy Neural an...
Differential Protection of Generator by Using Neural Network, Fuzzy Neural an...
Waqas Tariq
Neural Network Applications In Machining: A Review
Neural Network Applications In Machining: A Review
Ashish Khetan
Jack
Jack
Ericsson India Global Service Pvt Ltd
19 3 sep17 21may 6657 t269 revised (edit ndit)
19 3 sep17 21may 6657 t269 revised (edit ndit)
IAESIJEECS
Soft computing
Soft computing
CSS
Artifical neural networks
Artifical neural networks
alldesign
Classification Of Iris Plant Using Feedforward Neural Network
Classification Of Iris Plant Using Feedforward Neural Network
irjes
Artificial Neural Network and its Applications
Artificial Neural Network and its Applications
shritosh kumar
Deep learning for pose-invariant face detection in unconstrained environment
Deep learning for pose-invariant face detection in unconstrained environment
IJECEIAES
8421ijbes01
8421ijbes01
ijbesjournal
EEG Based Classification of Emotions with CNN and RNN
EEG Based Classification of Emotions with CNN and RNN
ijtsrd
109 115
109 115
Ijarcsee Journal
Neural Computing
Neural Computing
ESCOM
A Parallel Framework For Multilayer Perceptron For Human Face Recognition
A Parallel Framework For Multilayer Perceptron For Human Face Recognition
CSCJournals
EMG Diagnosis using Neural Network Classifier with Time Domain and AR Features
EMG Diagnosis using Neural Network Classifier with Time Domain and AR Features
IDES Editor
Kc3118711875
Kc3118711875
IJERA Editor
The second seminar
The second seminar
AhmedMahany
COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC APPROACHES FOR CRACK...
COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC APPROACHES FOR CRACK...
ijaia
Classification of physiological diseases using eeg signals and machine learni...
Classification of physiological diseases using eeg signals and machine learni...
eSAT Journals
Portfolio digital edmir amador
Portfolio digital edmir amador
Dulce Gomes
Knowledge management strategies in higher education
Knowledge management strategies in higher education
IAEME Publication
Parallel communicating flip pushdown automata systems communicating by stacks
Parallel communicating flip pushdown automata systems communicating by stacks
IAEME Publication
Face recognition across pose with estimation of pose parameters
Face recognition across pose with estimation of pose parameters
IAEME Publication
A design procedure for active rectangular microstrip patch antenna
A design procedure for active rectangular microstrip patch antenna
IAEME Publication
Summer school social media 2013 - Sociale Media vd Toekomst
Summer school social media 2013 - Sociale Media vd Toekomst
Arno Visser
Mais conteúdo relacionado
Mais procurados
19 3 sep17 21may 6657 t269 revised (edit ndit)
19 3 sep17 21may 6657 t269 revised (edit ndit)
IAESIJEECS
Soft computing
Soft computing
CSS
Artifical neural networks
Artifical neural networks
alldesign
Classification Of Iris Plant Using Feedforward Neural Network
Classification Of Iris Plant Using Feedforward Neural Network
irjes
Artificial Neural Network and its Applications
Artificial Neural Network and its Applications
shritosh kumar
Deep learning for pose-invariant face detection in unconstrained environment
Deep learning for pose-invariant face detection in unconstrained environment
IJECEIAES
8421ijbes01
8421ijbes01
ijbesjournal
EEG Based Classification of Emotions with CNN and RNN
EEG Based Classification of Emotions with CNN and RNN
ijtsrd
109 115
109 115
Ijarcsee Journal
Neural Computing
Neural Computing
ESCOM
A Parallel Framework For Multilayer Perceptron For Human Face Recognition
A Parallel Framework For Multilayer Perceptron For Human Face Recognition
CSCJournals
EMG Diagnosis using Neural Network Classifier with Time Domain and AR Features
EMG Diagnosis using Neural Network Classifier with Time Domain and AR Features
IDES Editor
Kc3118711875
Kc3118711875
IJERA Editor
The second seminar
The second seminar
AhmedMahany
COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC APPROACHES FOR CRACK...
COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC APPROACHES FOR CRACK...
ijaia
Classification of physiological diseases using eeg signals and machine learni...
Classification of physiological diseases using eeg signals and machine learni...
eSAT Journals
Mais procurados
(16)
19 3 sep17 21may 6657 t269 revised (edit ndit)
19 3 sep17 21may 6657 t269 revised (edit ndit)
Soft computing
Soft computing
Artifical neural networks
Artifical neural networks
Classification Of Iris Plant Using Feedforward Neural Network
Classification Of Iris Plant Using Feedforward Neural Network
Artificial Neural Network and its Applications
Artificial Neural Network and its Applications
Deep learning for pose-invariant face detection in unconstrained environment
Deep learning for pose-invariant face detection in unconstrained environment
8421ijbes01
8421ijbes01
EEG Based Classification of Emotions with CNN and RNN
EEG Based Classification of Emotions with CNN and RNN
109 115
109 115
Neural Computing
Neural Computing
A Parallel Framework For Multilayer Perceptron For Human Face Recognition
A Parallel Framework For Multilayer Perceptron For Human Face Recognition
EMG Diagnosis using Neural Network Classifier with Time Domain and AR Features
EMG Diagnosis using Neural Network Classifier with Time Domain and AR Features
Kc3118711875
Kc3118711875
The second seminar
The second seminar
COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC APPROACHES FOR CRACK...
COMPARISON OF ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC APPROACHES FOR CRACK...
Classification of physiological diseases using eeg signals and machine learni...
Classification of physiological diseases using eeg signals and machine learni...
Destaque
Portfolio digital edmir amador
Portfolio digital edmir amador
Dulce Gomes
Knowledge management strategies in higher education
Knowledge management strategies in higher education
IAEME Publication
Parallel communicating flip pushdown automata systems communicating by stacks
Parallel communicating flip pushdown automata systems communicating by stacks
IAEME Publication
Face recognition across pose with estimation of pose parameters
Face recognition across pose with estimation of pose parameters
IAEME Publication
A design procedure for active rectangular microstrip patch antenna
A design procedure for active rectangular microstrip patch antenna
IAEME Publication
Summer school social media 2013 - Sociale Media vd Toekomst
Summer school social media 2013 - Sociale Media vd Toekomst
Arno Visser
Facial expression using 3 d animation
Facial expression using 3 d animation
IAEME Publication
Job satisfaction in banking a study of private and public sector banks
Job satisfaction in banking a study of private and public sector banks
IAEME Publication
áLbum de fotos
áLbum de fotos
jdiazmo12388
Destaque
(9)
Portfolio digital edmir amador
Portfolio digital edmir amador
Knowledge management strategies in higher education
Knowledge management strategies in higher education
Parallel communicating flip pushdown automata systems communicating by stacks
Parallel communicating flip pushdown automata systems communicating by stacks
Face recognition across pose with estimation of pose parameters
Face recognition across pose with estimation of pose parameters
A design procedure for active rectangular microstrip patch antenna
A design procedure for active rectangular microstrip patch antenna
Summer school social media 2013 - Sociale Media vd Toekomst
Summer school social media 2013 - Sociale Media vd Toekomst
Facial expression using 3 d animation
Facial expression using 3 d animation
Job satisfaction in banking a study of private and public sector banks
Job satisfaction in banking a study of private and public sector banks
áLbum de fotos
áLbum de fotos
Semelhante a Early detection of adult valve disease mitral stenosis
Artificial neural network
Artificial neural network
Alexander Decker
Artificial Neural Networks in Human Life: Future Challenges and its Applications
Artificial Neural Networks in Human Life: Future Challenges and its Applications
IRJET Journal
Project Report -Vaibhav
Project Report -Vaibhav
Vaibhav Dhattarwal
40120140507007
40120140507007
IAEME Publication
40120140507007
40120140507007
IAEME Publication
Artificial Neural Network report
Artificial Neural Network report
Anjali Agrawal
Artificial neural network
Artificial neural network
GauravPandey319
Face Detection and Recognition using Back Propagation Neural Network (BPNN)
Face Detection and Recognition using Back Propagation Neural Network (BPNN)
IRJET Journal
Stock Prediction Using Artificial Neural Networks
Stock Prediction Using Artificial Neural Networks
ijbuiiir1
Neural Networks
Neural Networks
NikitaRuhela
neural networks
neural networks
joshiblog
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...
Sarvesh Kumar
Fuzzy Logic Final Report
Fuzzy Logic Final Report
Shikhar Agarwal
A04401001013
A04401001013
ijceronline
Dissertation character recognition - Report
Dissertation character recognition - Report
sachinkumar Bharadva
Artificial neural networks
Artificial neural networks
jsharath
StockMarketPrediction
StockMarketPrediction
sunandan barman
Wavelet-based EEG processing for computer-aided seizure detection and epileps...
Wavelet-based EEG processing for computer-aided seizure detection and epileps...
IJERA Editor
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...
IAEME Publication
Advanced applications of artificial intelligence and neural networks
Advanced applications of artificial intelligence and neural networks
Praveen Kumar
Semelhante a Early detection of adult valve disease mitral stenosis
(20)
Artificial neural network
Artificial neural network
Artificial Neural Networks in Human Life: Future Challenges and its Applications
Artificial Neural Networks in Human Life: Future Challenges and its Applications
Project Report -Vaibhav
Project Report -Vaibhav
40120140507007
40120140507007
40120140507007
40120140507007
Artificial Neural Network report
Artificial Neural Network report
Artificial neural network
Artificial neural network
Face Detection and Recognition using Back Propagation Neural Network (BPNN)
Face Detection and Recognition using Back Propagation Neural Network (BPNN)
Stock Prediction Using Artificial Neural Networks
Stock Prediction Using Artificial Neural Networks
Neural Networks
Neural Networks
neural networks
neural networks
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...
Fuzzy Logic Final Report
Fuzzy Logic Final Report
A04401001013
A04401001013
Dissertation character recognition - Report
Dissertation character recognition - Report
Artificial neural networks
Artificial neural networks
StockMarketPrediction
StockMarketPrediction
Wavelet-based EEG processing for computer-aided seizure detection and epileps...
Wavelet-based EEG processing for computer-aided seizure detection and epileps...
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...
Advanced applications of artificial intelligence and neural networks
Advanced applications of artificial intelligence and neural networks
Mais de IAEME Publication
IAEME_Publication_Call_for_Paper_September_2022.pdf
IAEME_Publication_Call_for_Paper_September_2022.pdf
IAEME Publication
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
IAEME Publication
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
IAEME Publication
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
IAEME Publication
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
IAEME Publication
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
IAEME Publication
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
IAEME Publication
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
IAEME Publication
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
IAEME Publication
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
IAEME Publication
GANDHI ON NON-VIOLENT POLICE
GANDHI ON NON-VIOLENT POLICE
IAEME Publication
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
IAEME Publication
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
IAEME Publication
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
IAEME Publication
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
IAEME Publication
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
IAEME Publication
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
IAEME Publication
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
IAEME Publication
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
IAEME Publication
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
IAEME Publication
Mais de IAEME Publication
(20)
IAEME_Publication_Call_for_Paper_September_2022.pdf
IAEME_Publication_Call_for_Paper_September_2022.pdf
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
GANDHI ON NON-VIOLENT POLICE
GANDHI ON NON-VIOLENT POLICE
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
Early detection of adult valve disease mitral stenosis
1.
INTERNATIONAL JOURNAL OF
COMPUTER ENGINEERING International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 3, Issue 3, October - December (2012), pp. 255-264 IJCET © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2012): 3.9580 (Calculated by GISI) ©IAEME www.jifactor.com EARLY DETECTION OF ADULT VALVE DISEASE–MITRAL STENOSIS USING THE ELMAN ARTIFICIAL NEURAL NETWORK Dr.Muhanned Alfarras Gulf University dr.muhanned@gulfuniversity.net Head of computer communications engineering Department ABSTRACT In complex, data-based prediction problems, such as medical diagnosis, the Elman Neural Net (ENN) has been applied for the automated detection of various diseases, such as mitral valve stenosis. This paper discusses the design and implementation of an automated classification system for heart diseases, based on ultrasonic devices. M-mode class images are applied to classify the degree of stenosis in the mitral valve. An artificial neural network (ANN), trained by the ENN, demonstrated good performance of the designed system. The system is applied in adult patients 20-60 years old, both male and female. Matlab software is used to design the system used to diagnose. The objective of the system used in our work is to diagnose mitral valve stenosis in samples of echocardiograph images for which there are difficulties in practical experiments in finding the optimal features by specialists who work in laboratories. Keywords: Image classification, Artificial neural network, Feature selection, Neuro-medical system, Kernal PCA, Elman Neural Net 1. INTRODUCTION Artificial neural networks, which are commonly referred to as “neural networks” (NNs), are simulations of the biological neural networks of the human brain that consist of a number of simple, highly interconnected elements to process information in its dynamic state and deliver it to external inputs; therefore, they are called “computing systems.” Artificial neural networks have been studded to understand and imitate human performance in solving complex problems. With incomplete or unrelated data that cannot be solved easy by conventional programming techniques or by human diagnosing, artificial neural networks (ANNs) are extremely useful for this type of information in the input data [1] because of the capability of ANNs to classify features, which refers to its ability to approximate functions and generalized them. [2] 255
2.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME All physicians are confronted during their training by the task of learning to diagnose. They must solve the problem of deducing certain diseases or of formulating a treatment based on more or less specified observations and knowledge, which is the standard knowledge provided by seminars, courses, and books. On one hand, medical knowledge becomes outdated quickly; on the other hand, this knowledge does not replace the surgeon’s own experience. For these tasks, certain basic difficulties must be taken into account. As the basis for a valid diagnosis, a sufficient number of experienced cases is reached only in the middle of a physician’s career, so that number has therefore not yet been reached by the end of academic training. This is especially true for rare or new diseases, with which experienced physicians are in the same situation as newcomers. Principally, humans do not resemble statistical computers, but they do have pattern recognition systems. Humans can recognize patterns or objects very easily, but they fail when probabilities must be assigned to observations. These principal difficulties are not widely understood by physicians. Also, studies revealing that approximately 50% of diagnoses are wrong have not impeded the self-conscience of some physicians [3]. 2. ARTIFICIAL NEURAL NETWORK (ANN) WITH MEDICAL IMAGES Neural network applications in computer-aided diagnosis represent the mainstream of computational intelligence in medical imaging. [4] Their penetration and involvement are almost comprehensive for all medical problems due to neural networks having the nature of adaptive learning from input information, and using a suitable learning algorithm, they can improve themselves in accordance with variety and changing in input content. Furthermore, neural networks have the capability to optimize the relationship between inputs and outputs via distributed computing, training, and processing, resulting in reliable solutions that are desired by specifications. Medical diagnosis often relies on visual inspection, and medical imaging provides the most important tool for facilitating such inspection and visualization [5-6]. The medical imaging field is very important because it offers much useful information for diagnosis and therapy. There are also many applications that use neural networks with Bayesian statistics (which can estimate the probability density of model parameters given the available data). A schematic diagram of an ANN, based on the principles of definition, is shown in Figure 1, in which the cell body within it is modeled by a linear activation function. The activation function attempts to enhance the signal contribution received through different dendrons. This action is assumed to be undertaken via signal conduction through resistive devices. The synapse within it is modeled by a non-linear inhabiting function to limit the amplitude of the signal processed by the cell body [7]. X w 1 1 out n w Inputs X2 Xi wi Output 2 w i =1 Net Xn n Dendrons Cell Body Axon Synapse Figure 1. An ANN based on the human brain [7] 256
3.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Artificial neural networks have been successfully used to recognize objects from their feature patterns. For the classification of patterns, neural networks should be trained prior to the phase of recognition. These processes of training can be classified into three typical categories, namely supervised, unsupervised, and reinforcement learning. These learning rules work with different structures of ANNs, as shown in Figure 2. feedback CONECTIONS Input Output Nodes Nodes I/p INPUT LAYER HIDDEN LAYER O/p OUTPUT LAYER a b c Figure 2. Different taxonomies of Anns: a. one-layer NN; b. multi-layer NN; c. Recurrent NN Elman Artificial Neural Networks are among the neural network learning algorithms widely used in medicine because they are conceptually simple and computationally efficient, and they often work successfully with noisy data. The use of ANNs in ultrasonic images (echocardiography), which is an important non-invasive methodology in clinical cardiology, helps the user to diagnose whether the tested object is normal or abnormal [8]. 3. PATTERN RECOGNITION IN NEURAL NETWORKS Human beings do not analyze different real-world situations as isolated facts, but they try to describe them in terms of patterns of related facts. Sometimes these relationships are implicit because they all refer to the same object. Other times, it is necessary to connect these characteristics explicitly to find relationships, because this skill is not only used for the perception of facts. In addition, methods of providing computers with this same pattern- processing skill of human beings have been sought. In some applications, pattern characteristics are best described by structural relationships, which depend on the size and location of information. The use of NN allows the operator to obtain a suitable choice for trainable pattern classification because of the NN’s capability of generalizing information, as well as its tolerance of noise and its power in computational systems, which consists of many simple processing elements connected together to perform tasks analogously to the performance of biological brains. Thus, like other machine learning problems, the data are divided into training set and a random testing set. The training data are used for feature selection; the testing data are used for the evaluation of the accuracy of the finally selected feature set [9]. 257
4.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME 4. MITRAL VALUVE STENOSIS Cardiovascular diseases are the most frequent cause of adult deaths in our time. These diseases can be divided into two main groups: 1. Congenital diseases (arterial septal defects, ASDs; ventricle septal defects, VSDs; tetralogy of Fallot, TTF; and heart failure, HF); and 2. Rheumatic disease (valvular disease: regurge and stenosis), as well as hypertensive and coronary diseases, which occur at different ages. Valvular heart disease is an important subject for study not only because it is often the primary cause of heart failure but also because there is much unnecessary confusion associated with it in the medical literature and in minds of many physicians. Among the most common rheumatic valve diseases is mitral stenosis (MS), which occurs when there is resistance to the flow of blood through the mitral aperture during diastolic filling of the left ventricle. This occurs if there is congenital stenosis, a thrombus, a trial myxoma, bacterial vegetation, or calcification of the valve [10]. In normal adults, the cross-sectional area of the mitral valve is 4-6 cm2, but when this area is reduced to approximately 2 cm2, then it represents mild MS. When the mitral opening is reduced to 1 cm2, then it is severe MS. Between 1 and 2 cm2 of indicates moderate MS. This disease never occurs before 20-30 years after rheumatic fever during childhood [11]. 5. RECOGNITION BY MEDICAL IMAGE SYSTEMS A research line of NNs is applied to recognize patterns in our images, which taken at Ibn Albetar and Ibn Alnafees hospitals. These images are shown in Figure 3. The computer imaging systems consisted of two primary component types, hardware and software. The hardware components can be divided into image acquisition system (Probe of Echocardiography - Voluson 530D; transducer) and a display and recording system (monitor). The software allows for manipulation of the images and performs any desired processing of the image data, which is in digital form. These images (100 images for training and100 images for testing) are obtained from patients and are compressed into a group at the size of 256 x 256 pixels. Thus, the analog images are converted into digital shapes, which are suitable for computing in a research program and which are represented as two-dimensional arrays of data with pixel values [12, 13, 14]. The developed system used an ANN to distinguish three sets of echocardiography images obtained for mitral valve stenosis (M-mode). These images describe the degree of stenosis (mild, moderate, or severe).[15]. 5. EXTRACT FEATURES The selection of adequate features plays an important role in the field of image segmentation, and with these techniques, it is possible to assess the dimensions of the problem with the objective of reducing its computational complexity. The small size and more relevant feature set will increase the classification accuracy and reduce the computation time. The output of feature selection algorithms is optimal, but such optimal feature sets may perform badly in some practical problems because minimal feature sets depend too heavily on the classification problem and the known data set [9]. 258
5.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME MS - MILD MS - MODERATE MS - SEVERE A MS - MILD MS - MODERATE MS - SEVERE B Figure 3. Types of learning and training data: A. Learning set; B. Testing set Feature selection must be based on the separability of classes. Features that make possible greater separability of classes are more significant and must be chosen for the implementation of the image segmentation process. Kernel principal component analysis (Kernal PCA) is used to assist the segmentation process by reducing segmented images’ (256 x 256) pixels to 3 PCA values. The main algorithm of PCA is: N ∑X i =1 i = 0. …………………………………………… (1) It operates by diagonalizing the covariance matrix. 1 N ∑ X i X Tj N i =1 ………………………..………………....... (2) In other words, it gives an Eigen decomposition of the covariance matrix λ v = CV , ..………………………………….…………(3) which can be rewritten as λ X Ti v = X iT CV ∀i ∈ [1, N ] ..…………………………………………... (4) In PCA d ≥ N dimensions if N points are used. X i , if it can map to N-dimensional space with Kroneker’s delta φ ( X i ) φ ( X i ) = δ ig , ……………………………….……………… (5) where φ : R d → R N & δ ig is Kroneker’s delta ( φ creates Elman-ly independent vectors). 259
6.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME In PCA φ ( X i ) are not independent in R N so K = k ( x, y ) = (φ ( x), φ ( y ) , ………………………..…………..…………….. (6) where k is the Grammian matrix in high-dimensional space. v1 = arg max var{v T x} = arg max E{(v T x) 2 ……….…………………………... (7) where v = 1 . We only need to compute the inner products in the high-dimensional space [12]. 6. SUGGESTED SYSTEM 6.1 METHOD AND EXPERIMENT Artificial neural networks are currently an important research area in medicine, and they can be useful in biomedical system application, especially in disease diagnosis and classification. Thus, we suggested a system consisting of Elman’s Neural Net divided into four layers, which are the input layer, hidden layer, connecting layer, and output layer, as shown in Figure 4. The architecture of Elman’s Neural Net allows for the enabling of the network to distinguish separate input patterns from the same input sequence, and the addition of the interior (connection) layer increases the capability of the processing of dynamic information by the network itself. Elman’s Neural Net must adapt to time-varying characteristics because it has the function of mapping dynamicity, resulting in simplifying of training the topology structure of Elman’s Neural Network shows that there are input (n), output (m), and (r) neurons in the hidden layer and connecting layer. The weight between the connecting layer and hidden layers are (w1,1), and the weight from the hidden layer to output layer is (w2,1); u(k-1) represents the input of the neural network, x(k) represents the output of the hidden layer, xc(k) represents the output of the connecting layer, y(k) represents the output of the neural network, and (f) represents the transfer function of the hidden layer [10]. 6.2 ALGORITHM OF ELMAN’S NEURAL NETWORK The algorithm of Elman Neural Net can be represented as follows X (k ) = f ( w2 xc (k ) + w1 (u (k − 1))) …………………………...……. (8) X c (k ) = x( k − 1) ………………………….……... (9) y (k ) = g ( w3 x( k )) ……………………………….. (10) g is the transfer function of the output layer and it is usually a linear function. S type function is commonly used and can be defined as: f ( x) = (1 + e − x ) ………………………..…….. (11) E is the error, and t k is the output vector of the object m 1 E= 2 ∑ (t k =1 k − yk ) 2 …………………………....…..(12) 260
7.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Figure 4 The main architecture of Elman’s Neural Network 7. TRAINING PROCEDURE To evaluate the performance of any neural network recognition system, the accuracy of the system can be calculated as: Number of correctly classified patterns Accuracy % = ………………………………..(13) Total number of paterrns The performance of the suggested system has been tested on data obtained from echocardiography device-type volution of 530 D with a 3.5 MHz central frequency transducer. Elman’s Neural Network is used for the training and testing classification process, in which 100 images are used for training, and 100 images used for testing, so Elman’s Neural Net and its efficiency can evaluate the performance of the microscope in testing images in greater detail. The median filter is also used to remove noise from each image; then, canny detection is used to detect their edges, while region growing is used to segment the images and to reduce segmentation by PCA from 256 x 256 pixels to 3 Kernal (PCA) values, as shown in Table 1. Table .1. The Kernal PCA values for research images Type Image 3-Kernal MILD -0.0623 - 0.0514 0.0560 MODER -0.0382 - 0.0800 ATE 0.0587 SEVERE -0.0441 -0.175 0.0596 Table 2 shows the recognition rates (classification results of training and testing images) of the ENN used in diagnostic systems of research for different iterations. 261
8.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Table 2. The recognition rates and iterations of one of ENNs Tmax 5000 5500 10000 10500 1500 15500 20000 20500 R% 90.32 90.51 89.67 89.91 91.92 93.54 96.80 96.97 Elman’s Neural Network gives the following error values: * Training data error = 0.098468 * Testing data error = 0.084284 The classifying results of training and testing images gave total classification accuracy of approximately 100% for training and 96.979% for testing images. The Elman Neural Network used provided both fast training and powerful mapping. Using the Matlab package (R 2009 a), this neural network has three input nodes, three hidden nodes, and one output node as shown in Figure 5. Hidden layer Input X1 W11 U1 W12 Output layer W21 X2 W22 U2 Y1 W23 W32 W13 W31 X3 U3 W33 X2 Connect X2 X2 Figure 5. The main architecture of the neural net used The combination of any two images with different types the same arrangement features led the diagnosing step to move away from its goal, and this situation referred to the behavior of the NN in its arrangement. The NN arrangement of any two objects, each of them equal in general features decreased the recognizing of training images; in addition, the error rate did not decrease to a suitable rate. To avoid this problem, which occurred in the beginning of the computing program used in this work, an increasing number of training images is used. As we mentioned earlier, the research used 100 images for training. 262
9.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME A three-layer neural network with one hidden layer, each layer in both input and output, contained one neuron for input data and output response, while five neurons in the hidden layer are for processing, as shown in the figure above. We used two neural networks for recognition and classification of diseases. The recognition network is used to identify the normal and abnormal cases; the classification network is used to classify the degree of abnormality. The input represented actual processing units, rather than the simple input ports, as would be the case with a strictly fed forward network. This network was trained alternatively from case data, which acted as continuous values. The relationships between the number of iterations and the learning rate for group A are shown in Figure 6. Root Root Mean Mean Square Square Error Error No.of iteration No.of iteration A B Root Mean Square Error No.of iteration C Figure 6. The performance of an artificial neural network during learning: A. MS-MILD; B. MS-MODERATE; C. MS-SEVERE 7 CONCLUSION The field of diagnosis is very important and covers a wide area of work. The implemented system provided good results and can applied in a good number of patients, so one advantage with the use of ANNs, compared to rule-based criteria, is the enhanced diagnostic performance and easy adjustability of the network outputs when used in different clinical situations. Several concluding remarks can be made according the provided results as follows. 263
10.
International Journal of
Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME 1- Although it is difficult to train hall images, it is very important to do this in the diagnosis of human diseases with regard to the degree of any disease because it must be the same degree in all patients who also have in common age, tall height, weight, health, etc. 2- Early diagnosis has several advantages, so using an ANN helps to diagnosis easily, especially when it is compared to programming and when using it in a package. 3- It is important to make a comparison between artificial and biological values to know the accuracy of the ANN’S work. 4- The classification technique that is used gave the highest accuracy, so Elman’s Neural Network shows the possibility of reducing input data to a learning step by extracting features dependent on regional grouping. This technique facilitates much research into image classification, especially when diagnosis is difficult for humans, as with medical images, which are increasingly being used in health care and medical research. REFERENCES 1. Lee, Jay, and Ben Wang. Computer-Aided Maintenance. New York: Kluwer Academic Publishers, 1999. 2. Gaetano, P. “Artificial Neural Networks in the Assessment of Respiratory Mechanisms,” unpublished doctoral dissertation, Faculty of Medicine, University of Uppsala, 2004. 3. Ge, J., Sahiner, B. Hadjiiski, L. M., Chan, H. P., Wei, J., Helvie, M. A., and Zhou, C. “Computer Aided Detection of Clusters of Micro Calcifications on Full field Digital Mammograms.” Medical Physics 33.8 (2006): 2975-88. 4. Chang, C. Y. “Contextual-based Hopfield neural network for medical image edge detection.” Optical Engineering 45.3 (2006): Art. No. 037006. 5. Qing Song. On the Weight Convergence of Elman Networks. IEEE Transactions on Neural networks, vol. 21, no. 3, March 2010 6. Middleton, INITIALS, and Damper, R. I. “Segmentation of Magnetic Resonance Images Using a Combination of Neural Networks and Active Contour Models.” Medical Engineering & Physics 26.1 (2004):71-86. 7. Suzuki, Kenji. “Artificial Neural Networks — Methodological Advances and Biomedical Applications.” Tech Janeza Rijeka, Croatia, 2011. 8. Cunn, Yann Lee et al. “Pattern Recognition & Neural Networks.” Bell Labs. 1998. 9. Muhanned Alfarras. “Enhance Image Transmission Based on DWT with Pixel Interleaver”. World Academy of Science, Engineering and Technology. Vol. (66), pp (563-567). 2012 10. Paulraj M. P, Mohd Shuhanaz Zanar Azalan, “Image Quality Assessment using Elman Neural Network Model and Interleaving Method” International Journal of Human Computer Interaction (IJHCI), Volume (3) : Issue (3) : 2012 11. Braun, Walt. “ Valvular Heart Disease” Text Book of Cardiovascular, 5th ed., 2001 12. Hymer P., “Extraction and Application of Secondary Crease Information in Fingerprint Recognition.” Dept. of Science and Technology, Linkopings Univ., Sweden, 2005. 13. L. Whei-Min and H. Chih-Ming, "A New Elman Neural Network-Based Control Algorithm for Adjustable-Pitch Variable-Speed Wind-Energy Conversion Systems," Power Electronics, IEEE Transactions on, vol. 26, pp. 473-481, 2011. 14. Luse, M., and Binstock A. Bitmapped Graphics Programming in C++. New York: Addison- Wesley, 2004. 15. Jie, INITIALS, Yao, INITIALS, and Cald, INITIALS. “Feature Selection for Flourscan Image Classification.” http://www.daneprairie.com, 2002. 264
Baixar agora