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تم العثور علي : 3776
 تم العثور علي : 3776
  
 
إعادة البحث

Thesis 2024.
Elshouky‘Hadeer Hussein Abdel-Mawla Hassan.
621 الفيزياء التطبيقية   621.3 الهندسة الكهربية  
The use of image classification in medical fields is one of the most important uses - including skin cancer image classification. Skin cancer is a major health problem across the world - and early identification is critical for successful treatment. Skin cancer - which is defined by abnormal skin cell development - is a common and dangerous disease worldwide. Despite advances in digital diagnosis tools - many present skin cancer detection technologies frequently fail to attain adequate levels of accuracy. Disease detection - computer-aided diagnosis - and patient risk identification rely heavily on computer vision. This is particularly true for skin cancer - which may be lethal if not detected early on. Several computer-aided diagnosis and detection systems have already been developed to do this.
In this dissertation - two approaches for classifying skin cancer images were examined and compared with the proposed methods. Machine Learning (ML) and Deep Learning (DL) are these two approaches. ML approaches include Artificial Neural Networks - Support Vector Machines - Naïve Bayes - and Decision Tree. Both Convolutional Neural Networks and Pretrained Deep Neural Networks (PDNN) were employed in the DL approach.
Two methods for detecting and binary classifying dermoscopic skin cancer images into benign and malignant were proposed. The first proposed method employs K-Nearest Neighbor (KNN) as a classifier with several PDNN serving as feature extractors - (KNN-PDNN). These networks include AlexNet - VGG-16 - VGG-19 - EfficientNet-B0 - ResNet-18 - ResNet-50 - ResNet-101 - DenseNet-201 - Inception-v3 - and MobileNet-v2. The second proposed method employs some PDNN with the Improved Grey Wolf Optimizer (I-GWO) - (PDNN-I-GWO). The PDNN used in this technique are AlexNet - ResNet-18 - SqueezeNet - ShuffleNet - and DarkNet-19.
The experiments of KNN-PDNN method used 4000 images from the ISIC archive dataset to train and test images. In certain PDNN - the KNN-PDNN method’s accuracy exceeded 99%. The PDNN-I-GWO method investigated two datasets: MED-NODE and DermIS. The outcomes showed that the proposed methods outperformed the other tested approaches. The highest accuracy achieved by this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively. The highest accuracy achieved with this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively.
The dissertation consists of five chapters as follows:
Chapter 1: Introduction
An introduction to the dissertation is given - explaining the importance of the research point and the goals it seeks to achieve - and an explanation of the problems found in some of the old techniques that we seek to improve in this thesis and the extent of their impact on classifying skin cancer images. This chapter also summarizes what the other chapters contain and the order in which they are reviewed in the thesis.
Chapter 2: Literature Review
This chapter covers background on skin cancer image classification and presents some previous works and methods used and their features and characteristics.
Chapter 3: Proposed System
The third chapter presents the proposed algorithms that were represented and applied in the dissertation. It reviews them in detail and discusses the additions and modifications that were made to achieve high accuracy. This chapter also presents the preprocessing of images before using them in the proposed methods. In addition - it includes different datasets for training and testing images.
Chapter 4: Experimental Results
It reviews all the experiments - their accompanying results - and details of the images that were used in the experiments. This dissertation also includes many comparisons between the proposed and modified algorithms that were used during the image classification process. This included using several methods and methods to evaluate and compare the performance of these algorithms.
Chapter 5: Conclusions and Recommendations for Future Work
It presents a summary of the results reached as well as some recommended points for future work that can be used to develop the work presented in this dissertation or related works - The use of image classification in medical fields is one of the most important uses - including skin cancer image classification. Skin cancer is a major health problem across the world - and early identification is critical for successful treatment. Skin cancer - which is defined by abnormal skin cell development - is a common and dangerous disease worldwide. Despite advances in digital diagnosis tools - many present skin cancer detection technologies frequently fail to attain adequate levels of accuracy. Disease detection - computer-aided diagnosis - and patient risk identification rely heavily on computer vision. This is particularly true for skin cancer - which may be lethal if not detected early on. Several computer-aided diagnosis and detection systems have already been developed to do this.
In this dissertation - two approaches for classifying skin cancer images were examined and compared with the proposed methods. Machine Learning (ML) and Deep Learning (DL) are these two approaches. ML approaches include Artificial Neural Networks - Support Vector Machines - Naïve Bayes - and Decision Tree. Both Convolutional Neural Networks and Pretrained Deep Neural Networks (PDNN) were employed in the DL approach.
Two methods for detecting and binary classifying dermoscopic skin cancer images into benign and malignant were proposed. The first proposed method employs K-Nearest Neighbor (KNN) as a classifier with several PDNN serving as feature extractors - (KNN-PDNN). These networks include AlexNet - VGG-16 - VGG-19 - EfficientNet-B0 - ResNet-18 - ResNet-50 - ResNet-101 - DenseNet-201 - Inception-v3 - and MobileNet-v2. The second proposed method employs some PDNN with the Improved Grey Wolf Optimizer (I-GWO) - (PDNN-I-GWO). The PDNN used in this technique are AlexNet - ResNet-18 - SqueezeNet - ShuffleNet - and DarkNet-19.
The experiments of KNN-PDNN method used 4000 images from the ISIC archive dataset to train and test images. In certain PDNN - the KNN-PDNN method’s accuracy exceeded 99%. The PDNN-I-GWO method investigated two datasets: MED-NODE and DermIS. The outcomes showed that the proposed methods outperformed the other tested approaches. The highest accuracy achieved by this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively. The highest accuracy achieved with this method is 100% and 97% in the MED-NODE and DermIS datasets - respectively.
The dissertation consists of five chapters as follows:
Chapter 1: Introduction
An introduction to the dissertation is given - explaining the importance of the research point and the goals it seeks to achieve - and an explanation of the problems found in some of the old techniques that we seek to improve in this thesis and the extent of their impact on classifying skin cancer images. This chapter also summarizes what the other chapters contain and the order in which they are reviewed in the thesis.
Chapter 2: Literature Review
This chapter covers background on skin cancer image classification and presents some previous works and methods used and their features and characteristics.
Chapter 3: Proposed System
The third chapter presents the proposed algorithms that were represented and applied in the dissertation. It reviews them in detail and discusses the additions and modifications that were made to achieve high accuracy. This chapter also presents the preprocessing of images before using them in the proposed methods. In addition - it includes different datasets for training and testing images.
Chapter 4: Experimental Results
It reviews all the experiments - their accompanying results - and details of the images that were used in the experiments. This dissertation also includes many comparisons between the proposed and modified algorithms that were used during the image classification process. This included using several methods and methods to evaluate and compare the performance of these algorithms.
Chapter 5: Conclusions and Recommendations for Future Work
It presents a summary of the results reached as well as some recommended points for future work that can be used to develop the work presented in this dissertation or related works

Book 2011.
Dimon, Theodore
ISBN: 9781583943205
Q العلم   QP الفسيولوجيا  
612 الفسيولوجيا البشرية  
Voice - Breathing exercises - Larynx - Voice culture - Voice - Breathing Exercises - Respiration - Voice Training
North Atlantic Books,

Book 2000
Bennett, Neville.
ISBN: 0335203361 ,0335203353
L التعليم   L تعليم ( عام )   LB نظرية وممارسة التعليم، والتدريس  
370 التعليم   370 التعليم   370.1 الفلسفة و النظريات   370.11 الأهداف و الغايات و القيمة  
Training - Occupational training - Education - Higher - Professional education - Business and education
Society for Research into Higher Education & Open University Press,

Book 2006.
Emmons, Shirlee
ISBN: 0195182421 ,9780195182422
M الموسيقى وكتب الموسيقى   M الموسيقى   MT تلقين و تدريس الموسيقى  
782 الموسيقى الصوتية  
Choral conducting - Choral singing - Voice culture - Voice Training
Oxford University Press,

Book 1928
Wiecking, Anna Margaret.
L التعليم   L تعليم ( عام )   LB نظرية وممارسة التعليم، والتدريس  
Manual training - Kindergarten - Creative activities and seat work
Ginn

Book 2022.
Clay, Ingrid S.,
ISBN: 9780744051285
R الطب   RA مظاهر الطب العامة  
613 الرعاية وتعزيز الصحة  
Interval training - Physical fitness - Interval training - Physical fitness

Book 2009
Yiannas, Frank.
ISBN: 9780387728667 ,
T التكنولوجيا   TX العلوم المنزلية  
363 خدمات المشاكل الاجتماعية الأخرى  
Food service - Food handling - Food industry and trade - Food service employees
Springer,

2006.
Berg, Shelly,
ISBN: 9780739043851 ,0739043854
M الموسيقى وكتب الموسيقى   M الموسيقى   MT تلقين و تدريس الموسيقى  
786 الآلات الموسيقية المفتاحية  
781 قواعد عامة وأشكال موسيقية  
Music theory - Musical instruments - Ear training
Alfred,

Book 2023.
Stone, Michael H.,
ISBN: 9781003096139
R الطب   RC ممارسة الطب  
613 الرعاية وتعزيز الصحة  
Sports - Exercise - Physical education and training

Book 2015.
ISBN: 9780736095716
797 الرياضة المائية والجوية  
Sport Physiologie - Sports Sciences - Swimming Training
Human Kinetics,


من 378
 







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