Computer Vision in Healthcare

Dr. Saurav Mallik is a Research Scientist in the Department of Pharmacology and Toxicology, The University of Arizona, Tucson, Arizona, USA. Prior to this he was Postdoctoral Fellow in Harvard T H Chan School of Public Health, University of Texas Health Science Center at Houston, and University of Miami Miller School of Medicine, USA. He obtained a PhD degree in the Department of Computer Science & Engineering from Jadavpur University, Kolkata, India in 2017 while his PhD was in Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India as a Junior Research Fellow. He is also a recipient of UGC Research Fellow and CSIR Research Associate, Government of India. He is also recipient of "Emerging Researcher In Bioinformatics" award from Bioclues & BIRD Award steering committee, India in the year 2020. He received two times Travel Grant Award for International Conference on Intelligent Biology and Medicine (ICIBM), 2018 at Los Angeles, California, USA and 2021 at Philadelphia, PA, USA. Dr Mallik has coauthored more than 225 research papers in various peer-reviewed international journals, proceedings and book chapters. He also has more than 20 authored/edited books with major publishing houses. He attended many conferences in the USA and India. He is currently an active member of Institute of Electrical and Electronics Engineers (IEEE), American Association for Cancer Research (AACR), and Association for Computing Machinery (ACM), USA and life member of BIOCLUES, India. He is associate editors of many journals such as Frontiers in Genetics, PloS One, BMC Bioinformatics, Frontiers in Bioinformatics, Frontiers in Applied Mathematics and Statistics, Archives of Medical Sciences, Mathematics, Electronics, Bioengineered, International Journal of Biomedical Imaging, Chemistry & Biodiversity, International Journal of Molecular Sciences, etc. He is a member of the international advisory committee of many reputed engineering colleges in India. His research areas include data mining, computational biology, bioinformatics, biostatistics and machine learning. Email: sauravmtech2@gmail.com, smallik@arizona.eduDr. Sandeep Kumar Mathivanan is an Assistant Professor in the School of Computing Science and Engineering, Galgotias University, Greater Noida, India. He received an M.S degree in Software engineering from the Vellore Institute of Technology (VIT), Vellore, India, in 2016, and the MTech (By Research) degree from VIT, Vellore, India, in 2020 and a PhD from the School of Information Technology and Engineering, VIT, Vellore, India, in 2023. He has more than 7 years of research experience. He is basically an academician, an author, a researcher, a reviewer of various global journals and international conferences. He is reviewer in many reputed journal like Heliyon, Multimedia tools, Soft computing, Transactions on Consumer Electronics, Optical and Quantum Electronics International journal of end-user computing and development, International journal of speech technology, International journal of system assurance engineering and management, Network modeling analysis in health informatics and bioinformatics., Progress in artificial intelligence, Computers in Biology and Medicine Computers and Electrical Engineering and Wireless personal communications. His research interests include machine learning, deep learning, remote sensing, big data. Email: sandeep.m@galgotiasuniversity.edu.in , sandeepkumarm322@gmail.comDr. Prabhu Jayagopal received a Bachelor’s degree in Information Technology from Vellore Engineering College, Vellore, India in 2004. He received a master’s degree in Computer Science and Engineering (2007) and PhD (2015) from Sathyabama University, Chennai, India, He has over 16 years of academic experience. Now he is a Professor in the School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore from 2009 to till date, He has published more than 85 papers in reputed journals and conferences. He is also involved in collaborative research projects with various national and international level organizations and research institutions. His research interests are software testing, machine learning, IoT, deep learning, blockchain, and big data. Email: jprabhuit@gmail.com, j.prabhu@vit.ac.inProf. (Dr.) Hong Qin obtained a PhD from the Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL, USA, in 2001. He is currently an associate professor at the School of Data Science, Department of Computer Science, Old Dominion University, USA. Previously, he was associate professor with the Department of Computer Science and Engineering, The University of Tennessee at Chattanooga, USA. He has published papers in more than 80 international peer-reviewed journals and conferences. He has NSF research grants that cover his research and publications. His research interests include cellular aging, COVID 19, mathematical modeling, machine learning, data science, bioinformatics, deep learning, and genomics. He uses computational and mathematical approaches to investigate biomedical and biological questions. Dr. Hong Qin is a recipient of NSF CAREER award 2015-2020. Qin's expertise includes graph reliability modeling; bioinformatics; computational genomics; mathematical modeling; systems biology; cellular aging; gene network analysis and modeling. Email: hqin@odu.edu Dr. Ben Othman Soufiane is an Assistant Professor of computer science at the King Faisal University, Saudi Arabia, from 2025. He received his Ph.D. degree in computer science from Manouba University, Tunisia in 2016 for his dissertation on “Secure data aggregation in wireless sensor networks. He also holds M.S. degrees from the Monastir University in 2012. My research interests focus on the Internet of Medical Things, Wireless Body Sensor Networks, Wireless Networks, Artificial Intelligence, Machine Learning and Big Data. Dr. Ben Othman has published more than 130 papers at reputed international journals, conferences, and book chapters. He is an Editorial Board Member in the different Journals and Conferences. He serves as an associate editor/academic editor for international journals including IEEE Access, IEEE Sensors, IEEE Internet of Things, Elsevier, Springer, Taylor & Francis, IGI, IET, Telecommunication Computing Electronics and Control, and Wiley. Dr. Ben Othman is a Technical Program Committee Member for more than a dozen of international conferences.Email: ben_oth_soufiene@yahoo.fr, sbenothman@kfu.edu.sa

• Preface. Transforming Healthcare with Computer Vision: Exploring Applications, Challenges, and Future Directions. Challenges and Future Directions in Computer Vision for Healthcare. HealthChain: Leveraging Blockchain for Scalable, Secure and Privacy-Preserving Healthcare Data Management. Fuzzy Multiset Based Decision Making Approaches and their Applications in Healthcare System. Adaptive Multi-Scale Feature Enhancement Algorithm for Improved Photoacoustic Imaging in Clinical Applications. Computer Vision-Based Pneumonia Diagnosis using Convolutional Neural Networks and Pretrained Models. Multi Modal Diagnostic Optimizer an Advanced Algorithm for Breast Cancer Detection using Thermographic Imaging. Enhancing Early Detection of Gestational-Pediatric Cardiovascular Diseases: A Novel Deep Learning-Based Approach. A Novel Artificial Intelligence Driven Adaptive Acoustic Profiling Technique for Noninvasive Cardiovascular Diagnostics. A Nonlinear SVM in Cardiovascular Diagnostics: Disentangling Myocardial Infarction with 2D Data Analysis. Revolutionizing Heart Disease Detection: Integrating Extended Whale Optimization with LSTM-Enhanced CNN For Advanced AI-Driven Diagnostics. Real-Time Prediction of Sepsis in ICU Patients with Long Short-Term Memory (LSTM) Networks. Real-Time Detection of Epileptic Seizures using a Hybrid Deep Learning Framework with Temporal CNN and BiLSTM. Development of a Real-Time Deep Learning-Based System for Automated Detection and Classification of Diabetic Retinopathy in Fundus Images. Deep Scan Imaging using Voxel Morph for High-Resolution Tissue Mapping and Quantitative Analys for Multimodal Medical Image Analysis and Disease Classification. Hybrid Convolutional Transformer Network (HCTNet) for Real-Time 4D Medical Imaging and Enhanced Early Disease Detection. Blockchain-Based Predictive Treatment by Enhancing Secure and Reliable Telemedicine in the Metaverse. Index.