Deep learning for cancer diagnosis. (2020)

• Record Type: Book
• Further Information: Note: Utku Kose, Jafar Alzubi, editors.
• Other Names: Kose, Utku, 1985-; Alzubi, Jafar
• Contents: Intro -- Foreword by Dr. Omer Deperlioglu -- Foreword by Dr. Jose Antonio Marmolejo-Saucedo -- Preface -- About This Book -- Contents -- Contributors -- 1 Fusion of Deep Learning and Image Processing Techniques for Breast Cancer Diagnosis -- 1.1 Cancer -- 1.1.1 Pathology -- 1.1.2 Breast Cancer -- 1.2 Diagnosis of Breast Cancer -- 1.2.1 Imaging Exams -- 1.3 Deep Learning and CNNs -- 1.3.1 CNN Architecture -- 1.4 Experimental Methodology -- 1.5 Conclusion -- References -- 2 Performance Evaluation of Classification Algorithms on Diagnosis of Breast Cancer and Skin Disease -- 2.1 Introduction 2.2 Datasets -- 2.3 Method -- 2.3.1 Deep Learning -- 2.3.2 Attribute Selection -- 2.3.3 Classification Algorithms -- 2.3.4 Performance Criteria -- 2.4 Experimental Results -- 2.5 Conclusion and Discussion -- References -- 3 Deep Learning Algorithms in Medical Image Processing for Cancer Diagnosis: Overview, Challenges and Future -- 3.1 Introduction -- 3.2 Stages in Cancer Diagnosis Using Medical Imaging -- 3.3 Types of Deep Learning Neural Network Architectures -- 3.3.1 Deep Supervised Learning Architectures -- 3.3.2 Deep Semi-supervised Learning Architectures 3.3.3 Deep Unsupervised Learning Architectures -- 3.4 Typical Deep Learning Architectures -- 3.4.1 Convolution Neural Network -- 3.4.2 Multi-scale Convolution Neural Network -- 3.5 Deep Learning Architectures for Cancer Diagnosis -- 3.6 Conclusion -- References -- 4 Classification of Canine Fibroma and Fibrosarcoma Histopathological Images Using Convolutional Neural Networks -- 4.1 Introduction -- 4.2 Data and Methodology -- 4.3 Proposed Convolutional Neural Network FibroNET -- 4.4 Results and Discussion -- References 5 Evaluation of Big Data Based CNN Models in Classification of Skin Lesions with Melanoma -- 5.1 Introduction -- 5.2 Classification and Diagnosis of Skin Lesions Using CNNs -- 5.2.1 Dermatoscopic Image Pre-processing -- 5.2.2 Dermatoscopic Image Segmentation -- 5.2.3 CNN-Based Image Classification -- 5.3 Experimental Setup -- 5.3.1 Data Description -- 5.3.2 New CNN Models -- 5.3.3 Pre-trained CNN Models and Their Modifications -- 5.4 Evaluation Results -- 5.5 Conclusion -- References -- 6 Combined Radiology and Pathology Based Classification of Tumor Types -- 6.1 Introduction -- 6.2 Methodology 6.2.1 Dataset Description -- 6.2.2 Processing Pathology Images -- 6.2.3 Processing Radiology Images -- 6.2.4 Support Vector Machine -- 6.3 Results -- 6.4 Conclusions -- References -- 7 Improved Deep Learning Techniques for Better Cancer Diagnosis -- 7.1 Computer Analysis Interpretation for Medical Visual Representation -- 7.1.1 Medical Imaging Types and Modalities -- 7.2 Review of Cancer Diagnosis via Novel Deep Learning -- 7.2.1 Deep Convolutional Transfer Learning-Based Neural Network -- 7.2.2 Data Augmented Convolved Neural Network -- 7.2.3 Unbalanced and Skew Supervised Deep Learning
• Publisher Details: Singapore : Springer
• Publication Date: 2020
• Copyright Date: 2021
• Extent: 1 online resource (300 pages)
• Subjects: 616.99/4075; Cancer -- Diagnosis -- Data processing; Machine learning; Cancer -- Diagnosis -- Data processing; Machine learning; Electronic books
• Languages: English
• ISBNs: 9789811563218
• Related ISBNs: 9789811563201
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.