In the ever-evolving realm of healthcare, Artificial Intelligence in Medicine emerges as a trailblazing guide, offering an extensive exploration of the transformative power of Artificial Intelligence (AI). Crafted by leading experts in the field, this book sets out to bridge the gap between theoretical understanding and practical application, presenting a comprehensive journey through the foundational principles, cutting-edge applications, and the potential impact of AI in the medical landscape.
This book embarks on a journey from foundational principles to advanced applications, presenting a holistic perspective on the integration of AI into diverse aspects of medicine. With a clear aim to cater to both researchers and practitioners, the scope extends from fundamental AI techniques to their innovative applications in disease detection, prediction, and patient care.
Distinguished by its practical orientation, each chapter presents actionable workflows, making theoretical concepts directly applicable to real-world medical scenarios. This unique approach sets the book apart, making it an invaluable resource for learners and practitioners alike.
Key Features:
• Comprehensive Exploration: From deep learning approaches for cardiac arrhythmia to advanced algorithms for ocular disease detection, the book provides an in-depth exploration of critical topics, ensuring a thorough understanding of AI in medicine.
• Cutting-Edge Applications: The book delves into cutting-edge applications, including a vision transformer-based approach for brain tumor detection, early diagnosis of skin cancer, and a deep learning-based model for early detection of COVID-19 using chest X-ray images.
• Practical Insights: Practical workflows and demonstrations guide readers through the application of AI techniques in real-world medical scenarios, offering insights that transcend theoretical boundaries.
This book caters to researchers, practitioners, and students in medicine, computer science, and healthcare technology. With a focus on practical applications, this book is an essential guide for navigating the dynamic intersection of AI and medicine. Whether you are an expert or a newcomer to the field, this comprehensive volume provides a roadmap to the revolutionary impact of AI on the future of healthcare.
List of Contributors
PART 1. Foundations of AI in healthcare
1. Exploring deep learning approaches for cardiac arrhythmia diagnosis
M S SUPRIYA, L YASHASWINI, AND K S ARVIND
2. Neural networks and LDA-based machine learning framework for the early detection of breast cancer
SAANJHI SARAOGI, SAKSHI SARAOGI, ASNATH VICTY PHAMILA Y, AND KALAIVANI KATHIRVELU
3. Advanced deep learning algorithms for early ocular disease detection using fundus images
SHUBHASHREE A, DIVYA B S, AND THOMPSON STEPHAN
PART 2. Disease detection and diagnosis
4. A vision transformer-based approach for brain tumor detection
PIYUSH KUMAR, RADHIKA GOYAL, SHUBHAM GARG, SHUCHI MALA, RONIT BALI, AND ANUKANSHA SHARMA
5. Early detection of skin cancer through human-computer collaboration
PIYUSH KUMAR, RISHI CHAUHAN, ACHYUT SHANKAR, AND THOMPSON STEPHAN
6. Improved mass detection in mammogram images with Dual Tree Complex Wavelet Transform and Fourier Descriptors
M KANCHANA, R NARESH, C N S VINOTH KUMAR, AND P PANDIARAJA
7. A deep learning-based model for early detection of COVID-19 using chest X-ray images
S PUNITHA, VAISHALI R KULKARNI, AND THOMPSON STEPHAN
8. Detection of seizure activity in fMRI images using deep learning techniques
ABHISHEK SAIGIRIDHARI, ABHISHEK MISHRA, ADITI MAHADWARE, AARYA TUPE, AND DHANALEKSHMI YEDURKAR
PART 3. Disease prediction and public health
9. Improving prediction accuracy for neo-adjuvant chemotherapy response in breast cancer through 3D image segmentation and deep learning techniques
K V RANJITHA AND T P PUSHPHAVATHI
10. A machine learning predictive framework for diabetes management using blood parameters
A POONGUZHALI, P RAMKUMAR, REJI THOMAS, S TAMIL SELVAN, AND ANGEL LATHA MARY
11. A combined neuro-fuzzy and Naive Bayes approach for swine flu disease prediction
P SANTHI, M SATHYA SUNDARAM, AND P PANDIARAJA
12. Enhancing decision-making in maternal public healthcare using a knowledge discovery-based predictive analytics framework
SHELLY GUPTA, JYOTI AGARWAL, AND DISHA MOHINI PATHAK
PART 4. Patient care and enhancements
13. Enhancing patient care and treatment through explainable AI: A gap analysis
SHYNI CARMEL MARY S, DHYANA SHARON ROSS, ANBUMANI BALA, AND JOE ARUN
14. Improved medical image captioning for chest X-rays using a hybrid VGG-ELECTRA model
J LIMSA JOSHI, J CHRISTINA, L REMEGIUS PRAVEEN SAHAYARAJ, V J SHARMILA, AND ASHWIN BALASUBRAMANIAN
15. Diagnosing Parkinson’s disease using a deep learning model based on electromyography sensors
P PADMA PRIYA DHARISHINI, B R KARTHIKEYAN, SURYA TEJAS V, JASH SINGH, SUMUKHA BHAT, AND G KARTHIK
16. Enhancing heart disease prediction with Hybridized KNN-MOPSO algorithm
R MANORANJITHAM, S PUNITHA, AND THOMPSON STEPHAN
Index
Biography
Thompson Stephan earned his Ph.D. in Computer Science and Engineering from Pondicherry University, India, in 2018. Currently serving as an Associate Professor in the Department of Computer Science & Engineering at Graphic Era Deemed to be University, Dehradun, Uttarakhand, India, he achieved recognition among the world's top 2% most influential scientists for 2023, a distinction jointly conferred by Elsevier and Stanford University, USA. Acknowledged for academic excellence during his master's degree, he secured a university rank. Additionally, he was honored with the Best Researcher Award-2020 and the Protsahan Research Award in 2023 by the IEEE Bangalore Section, India. His research interests primarily focus on implementing and applying artificial intelligence techniques in practical settings. He has authored numerous technical research papers published in renowned journals and conferences by IEEE, Elsevier, Springer, and others. Actively serving as a reviewer for esteemed international journals and working as a book editor, Thompson Stephan is dedicated to advancing the field.
