Computational Intelligence in Cancer Diagnosis

Progress and Challenges

Janmenjoy Nayak, Danilo Pelusi, Bighnaraj Naik, Manohar Mishra, Khan Muhammad, David Al-Dabass

• Publisher: Academic Press
• Year: 2023
• Print ISBN: 9780323852401
• eBook ISBN: 9780323903530
• Topic: Biological Sciences
• Subtopic: Biology
• Index: Biological Sciences

Computational Intelligence in Cancer Diagnosis: Progress and Challenges provides insights into the current strength and weaknesses of different applications and research findings on computational intelligence in cancer research. The book improves the exchange of ideas and coherence among various computational intelligence methods and enhances the relevance and exploitation of application areas for both experienced and novice end-users. Topics discussed include neural networks, fuzzy logic, connectionist systems, genetic algorithms, evolutionary computation, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems.The book's chapters are written by international experts from both cancer research, oncology and computational sides to cover different aspects and make it comprehensible for readers with no background on informatics.- Contains updated information about advanced computational intelligence, spanning the areas of neural networks, fuzzy logic, connectionist systems, genetic algorithms, evolutionary computation, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems in diagnosing cancer diseases- Discusses several cancer types, including their detection, treatment and prevention- Presents case studies that illustrate the applications of intelligent computing in data analysis to help readers to analyze and advance their research in cancer

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Contributors
• About the editors
• Foreword
• Preface
• List of Illustrations
• List of Tables
• Chapter 1 : The roadmap to the adoption of computational intelligence in cancer diagnosis: The clinical-radiological perspective
• Chapter 2 : Deep learning approaches for high dimension cancer microarray data feature prediction: A review
• Chapter 3 : Integrative data analysis and automated deep learning technique for ovary cancer detection
• Chapter 4 : Learning from multiple modalities of imaging data for cancer diagnosis
• Chapter 5 : Neural network for lung cancer diagnosis
• Chapter 6 : Machine learning for thyroid cancer diagnosis
• Chapter 7 : Machine learning-based detection and classification of lung cancer
• Chapter 8 : Deep learning techniques for oral cancer diagnosis
• Chapter 9 : An intelligent deep learning approach for colon cancer diagnosis
• Chapter 10 : Effect of COVID-19 on cancer patients: Issues and future challenges
• Chapter 11 : Empirical wavelet transform-based fast deep convolutional neural network for detection and classification of melanoma
• Chapter 12 : Convolutional neural networks and stacked generalization ensemble method in breast cancer prognosis
• Chapter 13 : Light-gradient boosting machine for identification of osteosarcoma cell type from histological features
• Chapter 14 : Deep learning-based computer-aided cervical cancer diagnosis in digital histopathology images
• Chapter 15 : Deep learning techniques for hepatocellular carcinoma diagnosis
• Chapter 16 : Issues and future challenges in cancer prognosis: (Prostate cancer: A case study)
• Chapter 17 : A novel cancer drug target module mining approach using nonswarm intelligence
• Index
• A