Computational Intelligence in Cancer Diagnosis

Progress and Challenges

1st Edition - April 12, 2023
Editors: Janmenjoy Nayak, Danilo Pelusi, Bighnaraj Naik, Mishra Manohar, Khan Muhammad, David Al-Dabass
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 2 4 0 - 1
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 3 5 3 - 0

Computational Intelligence in Cancer Diagnosis: Progress and Challenges provides insights into the current strength and weaknesses of different applications and research findings… Read more

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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.

Cover
Title page
Table of Contents
Copyright
Contributors
About the editors
Foreword
Preface
Part 1: Introduction to computational intelligence approaches
Part 2: Prediction of cancer susceptibility
Part 3: Advance computational intelligence paradigms
Part 1: Introduction to computational intelligence approaches
Chapter 1: The roadmap to the adoption of computational intelligence in cancer diagnosis: The clinical-radiological perspective
Abstract
Introduction
Radiomics and artificial intelligence for cancer diagnosis and treatment
Challenges and perspectives
Conclusion
References
Chapter 2: Deep learning approaches for high dimension cancer microarray data feature prediction: A review
Abstract
Introduction
Background
Microarray data
Machine learning
Deep learning
Critical analysis: Challenges and future trends
Software used
Critical analysis
Conclusion
References
Further reading
Chapter 3: Integrative data analysis and automated deep learning technique for ovary cancer detection
Abstract
Introduction
Related work
Methodology
Result analysis
Conclusion
References
Chapter 4: Learning from multiple modalities of imaging data for cancer diagnosis
Abstract
Introduction
Diagnosis in CT
Diagnosis in MRI
Diagnosis in X-ray
Diagnosis in UI
Diagnosis in PET
Medical imaging and artificial intelligence
Conclusion
References
Chapter 5: Neural network for lung cancer diagnosis
Abstract
Introduction
The basics knowledge of neural network
Neural network for detection of lung nodules
Application of neural network to medical image segmentation in diagnosis of lung cancer
Neural network for classification of lung cancer diagnosis
Conclusion and future directions
References
Chapter 6: Machine learning for thyroid cancer diagnosis
Abstract
Introduction
Literature survey
Thyroid cancer detection by ultrasound imaging and machine learning/deep learning
Machine learning/deep learning based thyroid cancer prediction using cytopathology whole-slide images and histopathology images
Thyroid gland disorders and machine learning approaches
Methods
Support Vector Machine
Multilayer Neural Network
Probabilistic Neural Network
Performance evaluation metrics
Autoencoders
Convolutional Neural Networks
Long short-term memory network
Results
Conclusion
References
Part 2: Prediction of cancer susceptibility
Chapter 7: Machine learning-based detection and classification of lung cancer
Abstract
Introduction
Related work
Problem definition
Proposed methodology
Result analysis and discussion
Conclusion
References
Chapter 8: Deep learning techniques for oral cancer diagnosis
Abstract
Acknowledgment
Introduction
Deep neural networks (DNNs)
Convolution neural networks (CNNs)
3D convolution neural networks (3D CNNs)
CNN-based methods for oral cancer diagnosis
Results and discussion
Current challenges and future research direction
Active and incremental learning for newly added samples and classes
Use of explainable artificial intelligence (XAI) for understanding data
Conclusion
References
Chapter 9: An intelligent deep learning approach for colon cancer diagnosis
Abstract
Introduction
Literature study
Materials and methods
Experimental setup
Result analysis
Conclusion
References
Chapter 10: Effect of COVID-19 on cancer patients: Issues and future challenges
Abstract
Introduction
Effect of COVID-19 on various types of cancer
Scenario of the impact of COVID-19 on cancer patients in various countries
Major challenges of oncology community and cancer patients during the COVID-19 pandemic
Discussion and future directions
Conclusion
References
Chapter 11: Empirical wavelet transform-based fast deep convolutional neural network for detection and classification of melanoma
Abstract
Introduction
Dataset
Continuous wavelet transform (CWT)
Discrete wavelet transform (DWT)
Empirical wavelet transform (EWT)
Fast deep convolutional neural network (fast-DCNN)
Result and discussion
Conclusion
References
Part 3: Advance computational intelligence paradigms
Chapter 12: Convolutional neural networks and stacked generalization ensemble method in breast cancer prognosis
Abstract
Introduction
Literature study
Methodology
Experimental setup
Results
Discussion
Conclusion
References
Chapter 13: Light-gradient boosting machine for identification of osteosarcoma cell type from histological features
Abstract
Introduction
Background information
Proposed model
Result and analysis
Conclusion
References
Chapter 14: Deep learning-based computer-aided cervical cancer diagnosis in digital histopathology images
Abstract
Acknowledgments
Introduction
Related work in cervical cancer detection
Proposed methodology
Description of dataset for experimental setup
Evaluation method and result obtained
Conclusion
References
Chapter 15: Deep learning techniques for hepatocellular carcinoma diagnosis
Abstract
Acknowledgment
Introduction
Diagnostic evaluation of HCC
Deep learning models
Deep learning approaches for diagnostic evaluation of HCC
Challenges and future perspectives
Conclusion
References
Further reading
Chapter 16: Issues and future challenges in cancer prognosis: (Prostate cancer: A case study)
Abstract
Introduction
Literature review
Proposed methodology
Materials and methods
Experimentation and result analysis
Conclusion
References
Further reading
Chapter 17: A novel cancer drug target module mining approach using nonswarm intelligence
Abstract
Acknowledgment
Nomenclature
Introduction
Related works
Methods and material
Proposed MR-CoCVFO
Experimental analysis
Conclusion
References
Index

Janmenjoy Nayak

Janmenjoy Nayak is an Assistant Professor, P.G. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo University, Baripada, Odisha, India. He has been a Gold Medallist in Computer Science twice in his career, and has been awarded the “Innovation in Science Pursuit for Inspired Research” (INSPIRE) Research Fellowship from the Department of Science & Technology, Government of India (at both Junior Research Fellow and Senior Research Fellow level) and Best Researcher Award from Jawaharlal Nehru University of Technology, Kakinada, Andhra Pradesh for the academic year 2018–19. He has received many other awards from national and international academic agencies. Dr. Nayak has edited 19 books and 8 special issues on the applications of computational intelligence, soft computing, and pattern recognition, which have been published by Springer and Inderscience. He has published more than 190 refereed articles in various book chapters, conferences, and peer-reviewed journals of Elsevier, Inderscience, Springer, the Institute of Electrical and Electronics Engineers (IEEE), and others. He has also served as a reviewer for more than 100 journals and conferences produced by the IEEE, the Association for Computing Machinery (ACM), Springer, Elsevier, Wiley, and Inderscience. He has 11 years of experience in both teaching and research. Dr. Nayak is a senior member of the IEEE and a life member of societies such as the Soft Computing Research Society (SCRS), the Computer Society of India (CSI India), the Orissa Information Technology Society (OITS), the Orissa Mathematical Society (OMS), and the International Association of Engineers (IAENG), Hong Kong. He has successfully conducted and is associated with 14 internationally renowned series conferences such as ICCIDM, HIS, ARIAM, CIPR, and SCDA. His areas of interest include data mining, nature-inspired algorithms, and soft computing.

Affiliations and expertise

Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo (MSCBD) University, Mayurbhanj, India

Danilo Pelusi

Danilo Pelusi is an Associate Professor in the Department of Communication Sciences, University of Teramo, where he received his PhD in Computational Astrophysics. He is an Editor of books for Springer and Elsevier, and an Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence, and IEEE Access, and was an Associate Editor of International Journal of Machine Learning and Cybernetics. He is a Guest Editor for Elsevier, Springer, and Inderscience journals and keynote speaker in several IEEE conferences; he is also an editorial board member of many journals. His research interests include fuzzy logic, neural networks, information theory, machine learning, and evolutionary algorithms.

Affiliations and expertise

Associate Professor, Faculty of Communication Sciences, University of Teramo, Teramo, Italy

Bighnaraj Naik

Bighnaraj Naik is an Assistant Professor in the Department of Computer Application, Veer Surendra Sai University of Technology (formerly UCE Burla), Odisha, India. He has published more than 100 research articles in various peer reviewed international journals, conferences, and book chapters. He has edited 10 books for publishers including Elsevier, Springer, and IGI Global. At present, he has more than 10 years of teaching experience in the field of computer science and information technology. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and his areas of interest include data science, data mining, machine learning, deep learning, computational intelligence (CI), and CI’s applications in science and engineering. He has served as Guest Editor of various special issues of journals such as Information Fusion (Elsevier), Neural Computing and Applications (Springer), Evolutionary Intelligence (Springer), International Journal of Computational Intelligence Studies (Inderscience), and International Journal of Swarm Intelligence (Inderscience). He is an active reviewer of various journals from publishers including IEEE Transactions, Elsevier, Springer, and Inderscience. Currently, he is undertaking a major research project as Principal Investigator, which is funded by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India.

Affiliations and expertise

Assistant Professor, Department of Computer Application, Veer Surrendra Sai University of Technology, Burla, India

Mishra Manohar

Manohar Mishra is an Associate Professor in the Department of Electronics and Electrical Engineering, Faculty of Engineering and Technology, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India. He received his PhD in Electrical Engineering, MTech in Power Electronics and Drives, and BTech in Electrical Engineering in 2017, 2012, and 2008, respectively. He has published more than 50 research papers in various peer-reviewed international journals and conferences. He has edited three research books, and has served as a reviewer for journal publishers such as the Institute of Electrical and Electronics Engineers (IEEE), Springer, Elsevier, and Inderscience. At present, he has more than 10 years of teaching experience in the field of electrical engineering. He is a senior member of the IEEE, and is currently guiding four PhD scholars. His areas of interest include power system analysis, power system protection, signal processing, power quality, distribution generation systems, and micro-grids. He has served as Convener and Volume Editor of the International Conference on Innovation in Electrical Power Engineering, Communication and Computing Technology (IEPCCT-2019, IEPCCT-2021) and the International Conference on Green Technology for Smart City and Society (GTSCS-2020). Currently, he is serving as Guest Editor for different journals such as International Journal of Power Electronics (Inderscience), International Journal of Innovative Computing and Application (Inderscience), and Neural Computing and Application (Springer).

Affiliations and expertise

Department of Electrical and Electronics Engineering, FET, Siksha O Anusandhan University, Bhubaneswar, India

Khan Muhammad

Khan Muhammad received his PhD in Digital Contents from Sejong University, South Korea in February 2019. He was an Assistant Professor in the Department of Software, Sejong University from March 2019 to February 2022. He is currently the director of Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab) and an Assistant Professor (Tenure-Track) in the Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, South Korea. His research interests include intelligent video surveillance, medical image analysis, information security, video summarization, multimedia data analysis, computer vision, and smart cities. He has registered 10 patents and contributed more than 220 papers in peer-reviewed journals and conference proceedings in his research areas. He is an Associate Editor/Editorial Board Member for more than 15 journals. He was among the most highly cited researchers in 2021 and 2022, according to the Web of Science (Clarivate).

Affiliations and expertise

Assistant Professor, Department of Interaction Science and Director of Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Sungkyunkwan University, Seoul, South Korea

David Al-Dabass

David Al-Dabass is Professor Emeritus and holds the personal Chair of Intelligent Systems in the Department of Computer Science, Nottingham Trent University, United Kingdom. He is a graduate of Imperial College, London University, holds a PhD, and has held postdoctoral and advanced research fellowships at the Control Systems Centre, University of Manchester Institute of Science and Technology (UMIST), Manchester University. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of the Institution of Engineering and Technology (IET), Institute of Mathematics and its Applications (IMA), and British Computer Society (BCS). He is the founder and Editor-in-Chief of International Journal of Simulation: Systems, Science and Technology, currently serves as President of the UK Simulation Society, and has previously served on the European Council for Modeling and Simulation as Director of the European Simulation Multiconference series. He has authored or coauthored more than 180 scientific publications on intelligent systems, modeling, and simulation, and has served as general Chair for more than 50 international conferences.

Affiliations and expertise

School of Computer Science, Nottingham Trent University, United Kingdom