دانلود کتاب Current Applications of Deep Learning in Cancer Diagnostics

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Cover Half Title Title Page Copyright Page Table of Contents List of Figures List of Tables Introduction Contributors Chapter 1: Contemporary Trends in the Early Detection and Diagnosis of Human Cancers Using Deep Learning Techniques 1.1 Introduction 1.2 Deep Learning Architectures Commonly Used for Cancer Diagnosis 1.2.1 Artificial Neural Networks (ANNs) 1.2.2 Convolutional Neural Networks (CNNs) 1.3 Use of Deep Learning in Cancer Diagnosis 1.4 Results and Discussion 1.5 Conclusion References Chapter 2: Cancer Data Pre-Processing Techniques 2.1 Introduction 2.2 Cancer Types 2.2.1 Cervical Cancer 2.2.2 Liver Cancer 2.2.3 Breast Cancer 2.2.4 Lung Cancer 2.2.5 Colorectal Cancer 2.2.6 Oral Cancer 2.3 Data Collection Modes 2.3.1 Magnetic Resonance Imaging (MRI) Data 2.3.2 Computed Tomography (CT) Scan Image Data 2.3.3 X-ray Image Data 2.3.4 Ultrasound Image Data 2.3.5 Gene Expression Data 2.3.6 Text Data 2.4 Common Pre-Processing Techniques Applicable for Cancer Data 2.4.1 MRI Data 2.4.1.1 Intensity Inhomogeneity Correction 2.4.1.2 Registration 2.4.1.3 Segmentation 2.4.1.4 Slice Timing Correction 2.4.1.5 Motion Correction 2.4.1.6 Nuisance Variable Removal 2.4.1.7 Filtering 2.4.1.8 Spatial Smoothing 2.4.2 CT Scan Image Data 2.4.2.1 Denoising 2.4.2.2 Interpolation 2.4.2.3 Registration 2.4.2.4 Normalization 2.4.3 X-ray Image Data 2.4.3.1 Adaptive Contrast Enhancement 2.4.3.2 Region Localization 2.4.4 Ultrasound Image Data 2.4.4.1 Deblurring 2.4.4.2 Resolution Enhancement 2.4.4.3 Denoising 2.4.5 Gene Expression Data 2.4.5.1 Scale Transformations 2.4.5.2 Management of Missing Values 2.4.5.3 Replicate Handling 2.4.6 Text Data 2.4.6.1 Data Cleaning 2.4.6.2 Data Reduction 2.4.6.3 Normalization 2.4.6.4 Discretization and Concept Hierarchy Generation 2.5 Conclusions References Chapter 3: A Survey on Deep Learning Techniques for Breast, Leukemia, and Cervical Cancer Prediction 3.1 Introduction 3.1.1 Breast Cancer 3.1.2 Leukemia 3.1.3 Cervical Cancer 3.2 Literature Survey 3.2.1 Deep Learning Methods for Leukemia Prediction 3.2.2 Machine Learning Methods for Cervical Cancer Prediction 3.2.3 Deep Learning Methods for Breast Cancer Prediction 3.3 Conclusion References Chapter 4: An Optimized Deep Learning Technique for Detecting Lung Cancer from CT Images 4.1 Introduction 4.2 Literature Review 4.3 Design Approach and Details 4.3.1 Basic CNNs 4.3.2 Convolutional Layer and Sub-Sampling Method 4.4 Proposed CNN Architecture 4.4.1 Data Augmentation 4.5 Experimental Analysis 4.5.1 Parameter Setting 4.6 Conclusion References Chapter 5: Brain Tumor Segmentation Utilizing MRI Multimodal Images with Deep Learning 5.1 Introduction 5.2 Material and Methods 5.2.1 Pre-Processing 5.2.2 Tumor Representation in Each Slice 5.2.3 Finding the Expected Area of the Tumor 5.2.4 Deep Learning Architecture 5.2.5 Proposed Structure 5.2.6 Distance-Wise Attention Module 5.2.7 Cascade CNN Model 5.3 Experiments 5.3.1 Data and Implementation Details 5.3.2 Evaluation Measure 5.3.3 Experimental Results 5.4 Conclusion References Chapter 6: Detection and Classification of Brain Tumors Using Light-Weight Convolutional Neural Network 6.1 Introduction 6.2 Related Works 6.3 Dataset Detail 6.4 Methodology 6.4.1 Detection of Brain Tumor 6.4.2 Classification of Brain Tumor 6.5 Results and Discussions 6.5.1 Comparison of the Proposed Approach with Other Light-Weight Models 6.6 Conclusion References Chapter 7: Parallel Dense Skip-Connected CNN Approach for Brain Tumor Classification 7.1 Introduction 7.2 Parallel Dense Skip-Connected CNN (PDSCNN) 7.3 Results and Discussion 7.3.1 Network Training Parameters 7.3.2 Brain Tumor MRI Dataset 7.3.3 Tumor Identification Accuracies 7.3.4 Confusion Matrices 7.4 Conclusion References Chapter 8: Liver Tumor Segmentation Using Deep Learning Neural Networks 8.1 Introduction 8.2 Prior Work 8.3 Proposed Solution and Architecture 8.3.1 Data Set Used 8.3.2 FastAI Library 8.3.3 U-Net Architecture 8.3.4 Employed Dynamic U-Net with ResNet34 Encoder 8.3.5 Data Preprocessing 8.3.6 Proposed Architecture and Methodology 8.3.7 Model Training Metrics 8.4 Model Analysis 8.5 Model Specifications and Runtime Analysis 8.6 Performance Evaluation 8.6.1 Dice Similarity Coefficient 8.6.2 Comparative Analysis 8.7 Conclusion References Chapter 9: Deep Learning Algorithms for Classification and Prediction of Acute Lymphoblastic Leukemia 9.1 Introduction 9.2 Related Works 9.3 Dataset Description 9.4 Methodology 9.5 Results and Discussion 9.6 Conclusion Details of Authors References Note Chapter 10: Cervical Pap Smear Screening and Cancer Detection UsingDeep Neural Network 10.1 Introduction 10.2 Related Work 10.3 Methodology 10.4 Dataset 10.4.1 Normal 10.4.2 Abnormal 10.4.3 Benign 10.5 Experimental Results 10.6 Conclusion References Chapter 11: Cancer Detection Using Deep Neural Network Differentiation of Squamous Carcinoma Cells in Oral Pathology 11.1 Histopathology – A Review 11.2 Computer Vision in Feature Extraction 11.3 Deep Neural Nets for Cancer Diagnosis 11.4 Differential Diagnosis in Oral Pathology 11.4.1 Convolutional Neural Network 11.4.1.1 Convolution Operation 11.4.1.2 Pooling Operation 11.4.1.3 Decision Function 11.4.1.4 Normalization 11.4.1.5 Dropout 11.4.1.6 Fully Connected Layer 11.5 Automated Detection and Grading of Squamous Cell Carcinoma forDiagnosis of Oral Cancer 11.5.1 Problem Statement 11.5.2 Objectives 11.5.3 Methodology/Experimental Design and Sampling Strategy 11.5.4 Methodology/Experimental Design and Sampling Strategy 11.5.5 Methodology/Experimental Design 11.5.6 Methodology/Experimental Design 11.5.7 Performance Measures and Metrics 11.5.7.1 Recall 11.5.7.2 Dice Similarity Coefficient (DSC) 11.5.7.3 Intersection over Union (IOU) 11.5.7.4 Confusion Matrix (CM) 11.5.7.5 Accuracy 11.5.7.6 F1 Score 11.6 Research Challenges in Digital Pathology 11.7 Conclusion References Chapter 12: Challenges and Future Scopes in Current Applications of Deep Learning in Human Cancer Diagnostics 12.1 Introduction 12.1.1 Challenges in Deep Learning 12.1.2 Advantages of Deep Learning 12.1.3 Current Application of Deep Learning in Cancer Prognosis 12.2 Neural Networks and Their Types 12.2.1 Non-Feature-Extracted NN Models 12.2.2 Creation of Fully Connected NNs by Extracting Features from Gene Expression Data 12.2.3 CNN-Based Models 12.2.4 Cancer Imaging with Convolutional Neural Networks 12.2.5 Digital Pathology 12.2.6 Electronic Medical Records (EMRs) 12.2.7 Deep Learning and Artificial Neural Networks (DL) in Healthcare 12.3 Challenges and Opportunities of Deep Learning in Cancer Diagnostics 12.3.1 Enhancement of Features 12.3.1.1 Federated Inference 12.3.1.2 Model Privacy 12.3.1.3 Incorporating Expert Knowledge 12.3.1.4 Temporal Modeling 12.3.1.5 Interpretable Modeling 12.4 Conclusion 12.5 Acknowledgment 12.6 Conflict of Interest 12.7 Funding Statement References Index