This book examines deep learning-based approaches in the field of cancer diagnostics, as well as pre-processing techniques, which are essential to cancer diagnostics. Topics include introduction to current applications of deep learning in cancer diagnostics, pre-processing of cancer data using deep learning, review of deep learning techniques in oncology, overview of advanced deep learning techniques in cancer diagnostics, prediction of cancer susceptibility using deep learning techniques, prediction of cancer reoccurrence using deep learning techniques, deep learning techniques to predict the grading of human cancer, different human cancer detection using deep learning techniques, prediction of cancer survival using deep learning techniques, complexity in the use of deep learning in cancer diagnostics, and challenges and future scopes of deep learning techniques in oncology.
Author(s): Jyotismita Chaki, Aysegul Ucar
Publisher: CRC Press
Year: 2023
Language: English
Pages: 188
City: Boca Raton
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
