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Computational Intelligence in Cancer Diagnosis. Progress and Challenges

  • Book
  • October 2022
  • Elsevier Science and Technology
  • ID: 5527270

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.

Table of Contents

SECTION 1. Introduction to Computational Intelligence Approaches 1. The Roadmap to the Adoption of Computational Intelligence in Cancer Diagnosis: the Clinical-Radiological Perspective 2. Deep Learning Approaches for High Dimensional Cancer Microarray Data Feature Prediction: A Review 3. Integrative Data Analysis and Automated Deep Learning Technique for Ovary Cancer Detection 4. Learning from Multiple Modalities of Imaging Data for Cancer diagnosis 5. Neural Network for Lung Cancer Diagnosis 6. Machine Learning for Thyroid Cancer Diagnosis

SECTION 2. Prediction of Cancer Susceptibility 7. Machine Learning based Detection and Classification of Lung Cancer 8. Deep learning techniques for Oral Cancer Diagnosis 9. An Intelligent Deep Learning approach for Colon cancer diagnosis 10. Effect of COVID-19 on cancer patients: Issues and Future Challenges 11. Empirical Wavelet Transform-based Fast Deep Convolutional Neural Network for Detection and Classification of Melanoma

SECTION 3. Advance Computational Intelligence Paradigms 12. Convolutional neural networks and stacked generalization ensemble method in breast cancer prognosis 13. An Advance Ensemble Learning model for Cancer diagnosis 14. Deep Learning Based Computer-Aided Cervical Cancer Diagnosis in Digital histopathology Images 15. Deep Learning Techniques for Hepatocellular Carcinoma Diagnosis 16. Issues and Future Challenges in Cancer prognosis: (Prostate cancer: A case study) 17. A Novel Cancer Drug Target Module Mining Approach using Non-Swarm Intelligence

Authors

Janmenjoy Nayak Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo (MSCBD) University, Mayurbhanj, India. Dr. Janmenjoy Nayak is working as an Assistant Professor, Department of Computer Science, Maharaja Sriram Chandra BhanjaDeo University, Baripada, Odisha, India. Being two times Gold Medallist in Computer Science in his career, he has been awarded with INSPIRE Research Fellowship from Department of Science & Technology, Govt. of India (both as JRF and SRF level) and Best researcher award from Jawaharlal Nehru University of Technology, Kakinada, AP for the AY: 2018-19 and many other awards from national and international academic agencies. He has edited 21 books and 8 Special Issues on the applications of Computational Intelligence, Soft Computing, and pattern recognition, published by Springer, Inderscience International publications. He has published more than 170+ referred articles in various book chapters, conferences and International repute peer reviewed journals of Elsevier, Inderscience, Springer, IEEE etc. He is serving as Associate editor of IEEE ACCESS journal and reviewer for over 100 well-reputed journals and conferences, from IEEE, ACM, Springer, Elsevier, Wiley, and Inderscience publishers. He has a total twelve years of experience in both teaching and research. He is the senior member of IEEE and life member of some of the reputed societies like Soft Computing Research Society (SCRS), CSI India, Orissa Information Technology Society (OITS), Orissa Mathematical Society (OMS), IAENG (Hongkong) etc. He has successfully conducted and is being associated with 14 International repute series conferences like ICCIDM, HIS, ARIAM, CIPR, SCDA etc. His area of interest includes data mining, nature inspired algorithms and Applied Artificial Intelligence. Danilo Pelusi Associate Professor, Faculty of Communication Sciences, University of Teramo, Teramo, Italy. Danilo Pelusi received the degree in Physics from the University of Bologna (Italy) and the Ph.D. degree in Computational Astrophysics from the University of Teramo (Italy). Currently, he is an Associate Professor of Computer Science at the Department of Communication Sciences, University of Teramo. Editor of Springer and Elsevier books and Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence (2017-2020), IEEE Access (2018-present) and IEEE Transactions on Neural Networks and Learning Systems (2022-present), he is Guest Editor for Elsevier, Springer, MDPI and Hindawi journals. Keynote speaker, Guest of Honor and Chair of IEEE conferences, he is inventor of patents on Artificial Intelligence. His research interests include Fuzzy Logic, Neural Networks, Information Theory, Machine Learning and Evolutionary Algorithms. Bighnaraj Naik Assistant Professor, Department of Computer Application, Veer Surrendra Sai University of Technology, Burla, India. 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 reputed peer reviewed International Journals, Conferences and Book Chapters. He has edited ten books from various international publishers such as Elsevier, Springer and IGI Global. At present, he has more than ten years of teaching experience in the field of Computer Science and Information Technology. He is a member of IEEE and his area of interest includes Data Science, Data Mining, Machine Learning, Deep Learning, Computational Intelligence and its applications in Science and Engineering. He has been serving as Guest Editor of various journal special issues in 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) etc. He is an active reviewer of various reputed journals from reputed publishers including IEEE Transactions, Elsevier, Springer and Inderscience etc. Currently, he is undertaking a major research project in the capacity of Principal Investigator, which is funded by Science and Engineering Research Board (SERB), Dept. of Science & Technology (DST), Govt. of India. Manohar Mishra Department of Electrical and Electronics Engineering, FET, Siksha O Anusandhan University, Bhubaneswar, India. Dr. Manohar Mishra is an Associate Professor in the Department of Electronics & Electrical Engineering Department, under the Faculty of Engineering & Technology, Siksha "O” Anusandhan University, Bhubaneswar. He received his Ph.D. in Electrical Engineering, M.Tech. in Power Electronics and Drives and B.Tech. in Electrical engineering in 2017, 2012 and 2008, respectively. He has published more than 50 research papers in various reputed peer reviewed International Journals, Conferences. He has edited 3 research Books. He has served as reviewers for various reputed Journal publishers such as 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 IEEE. He is currently guiding Four Ph.D. Scholars. His area of interest includes power system analysis, power system protection, signal processing, power quality, distribution generation system, Micro-grid. He has served as Convener &Volume Editor of International Conference on Innovation in Electrical Power Engineering, Communication and Computing Technology (IEPCCT-2019, IEPCCT-2021) and International Conference on Green Technology for Smart City and Society (GTSCS-2020). Currently, he is serving as Guest editor in different journals such as International Journal of Power Electronics (Inderscience Publisher) and International Journal of Innovative Computing and Application (Inderscience Publisher), Neural Computing and Application (Springer). Khan Muhammad Assistant Professor, Department of Interaction Science and Director of Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Sungkyunkwan University, Seoul, South Korea. Khan Muhammad (S'16-M'18) received his PhD degree in Digital Contents from Sejong University, Republic of Korea. He is currently working as an Assistant Professor at the Department of Interaction Science and is the Director of Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), 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 IoT/IoMT. He has registered 8 patents in South Korea (7)/Australia (1) and has contributed 170+ papers in peer-reviewed journals and conference proceedings in his areas of research. His contributions have received 4347 citations with an H-index 39 according to the Web of Science. Based on citations and other metrics, he was selected among top 100000 scientists around the globe by Stanford Researchers List in 2020 and 2021. He is an Associate Editor/Editorial Board Member of 13 journals. He is also an Editor of the Book Series on "Intelligent Data-Driven Technology for Sustainability”. He is serving as a reviewer for over 120 well-reputed journals and conferences, from IEEE, ACM, Springer, Elsevier, Wiley, and SAGE publishers. He has edited a book titled "Multimedia Security: Algorithm Development, Analysis and Applications” with Springer. He has edited several special issues at reputed journals such as Elsevier Neural Networks and Wily Concurrency and Computation: Practice and Experience and is currently acting as LGE/GE for 7+ special issues. David Al-Dabass School of Computer Science, Nottingham Trent University, United Kingdom. David Al-Dabass is Professor Emeritus and holds the personal chair of Intelligent Systems in the School of Science & Technology, Nottingham Trent University, United Kingdom. He is a graduate of Imperial College, London University, holds a PhD and has held post-doctoral and advanced research fellowships at the Control Systems Centre, UMIST, Manchester University. He is member of IEEE and Fellow of the IET, IMA and BCS. He is founder and editor-in-chief of the 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 Modelling and Simulation as director of the European Simulation Multi-conference series. He has authored or co-authored over 180 scientific publications in intelligent systems, modelling and simulation and served as general chair for over 50 international conferences.