Deep Learning in Cancer Diagnostics 1st Edition by Mohd Hafiz Arzmi, Anwar Abdul Majeed, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Hong Seng Gan, Ismail Mohd Khairuddin, Ahmad Fakhri Ab Nasir ISBN 9789811989360 9811989362

Deep Learning in Cancer Diagnostics 1st Edition by Mohd Hafiz Arzmi, Anwar Abdul Majeed, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Hong Seng Gan, Ismail Mohd Khairuddin, Ahmad Fakhri Ab Nasir – Ebook PDF Instant Download/Delivery: 9789811989360 ,9811989362
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ISBN 10: 9811989362
ISBN 13: 9789811989360
Author: Mohd Hafiz Arzmi, Anwar Abdul Majeed, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Hong Seng Gan, Ismail Mohd Khairuddin, Ahmad Fakhri Ab Nasir

Cancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries for individuals less than 70 years old, which is alarming. In addition, cancer affects socioeconomic development as well. The diagnostics of cancer are often carried out by medical experts through medical imaging; nevertheless, it is not without misdiagnosis owing to a myriad of reasons. With the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is no longer far-fetched. In this brief, the diagnosis of  four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer.

Deep Learning in Cancer Diagnostics 1st Edition Table of contents:

Chapter 1: Introduction to Deep Learning in Healthcare

• Overview of Artificial Intelligence and Machine Learning

• Evolution of Deep Learning Techniques

• Deep Learning in Healthcare and Medical Imaging

• Scope of Deep Learning in Cancer Diagnostics

Chapter 2: Fundamentals of Deep Learning

• Introduction to Neural Networks

• Supervised and Unsupervised Learning

• Convolutional Neural Networks (CNNs)

• Recurrent Neural Networks (RNNs)

• Transfer Learning in Medical Applications

• Challenges in Deep Learning Models

Chapter 3: Cancer Biology and Diagnostics

• Basic Understanding of Cancer

• Traditional Cancer Diagnosis Techniques

• Advancements in Diagnostic Imaging

• The Role of Histopathology and Radiology in Cancer Diagnosis

• The Need for Computational Methods in Cancer Diagnosis

Chapter 4: Medical Imaging and Deep Learning in Cancer Diagnosis

• Medical Imaging Modalities (X-ray, CT, MRI, PET, Ultrasound)

• Image Preprocessing for Deep Learning

• Image Segmentation Techniques

• Feature Extraction and Classification

• Deep Learning Models for Tumor Detection and Classification

• Case Studies in Cancer Imaging

Chapter 5: Deep Learning in Histopathology and Cytology

• Histopathology in Cancer Diagnosis

• Digital Pathology and Whole Slide Imaging

• CNNs in Histopathology Image Analysis

• Deep Learning Models for Cancer Cell Detection

• Applications in Breast Cancer, Lung Cancer, and Prostate Cancer

Chapter 6: Genomic Data and Cancer Diagnostics

• Introduction to Genomic Data in Cancer Research

• Gene Expression Profiling and Cancer Subtypes

• Deep Learning Models for Genomic Data Analysis

• Integration of Genomic and Imaging Data

• Predictive Models for Cancer Prognosis and Treatment Response

Chapter 7: Challenges in Deep Learning for Cancer Diagnosis

• Data Imbalance and Augmentation

• Interpretability of Deep Learning Models

• Overfitting and Model Validation

• Privacy and Ethical Considerations in Medical Data

• Regulatory Challenges and Clinical Adoption

Chapter 8: Current Applications and Case Studies in Cancer Diagnosis

• Early Detection of Breast Cancer

• Lung Cancer Screening and Detection

• Skin Cancer Detection via Dermoscopy Images

• Glioma Detection from MRI Scans

• Personalized Treatment Predictions

• Clinical Success Stories and Lessons Learned

Chapter 9: Future Directions in Deep Learning for Cancer Diagnostics

• Emerging Trends in AI and Deep Learning

• Integration with Other Diagnostic Modalities

• The Role of Explainable AI in Healthcare

• The Potential of Multimodal Data Fusion

• Challenges and Opportunities for Clinical Translation

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Tags: Mohd Hafiz Arzmi, Anwar Abdul Majeed, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Hong Seng Gan, Ismail Mohd Khairuddin, Ahmad Fakhri Ab Nasir, Cancer Diagnostics