Lightweight, Real-time Deep Learning Models for Healthcare Applications

Dr. Tina Babu is an accomplished engineering educator and researcher currently serving as Assistant Professor in the Department of Computer Science and Engineering at Alliance University, Bengaluru. With over 11 years of academic experience, she specializes in image processing, artificial intelligence, machine learning, and pattern recognition. Dr. Babu holds a Ph.D. focused on automated colon cancer screening and grading of histopathological images, demonstrating her expertise in applying AI to medical diagnostics. Her research contributions include over 50 publications in prestigious journals and conferences, with particular emphasis on medical image analysis and deep learning applications. She has also secured multiple patents and received research funding from SERB. Dr. Babu maintains an impressive academic profile with an h-index of 10. Her teaching portfolio encompasses advanced courses in Computer Vision, Machine Learning, and Database Management Systems, reflecting her commitment to nurturing the next generation of technology professionals. Dr. Rekha R. Nair is a seasoned academic and researcher with extensive experience in computer science, specializing in medical image processing, machine learning, data science, and artificial intelligence. She currently serves as an Assistant Professor in the Department of Computer Science and Engineering at Alliance University, Bengaluru. Previously, she was a faculty member at Dayananda Sagar University and completed her Ph.D. at Amrita Vishwa Vidyapeetham, where her research focused on developing algorithms for multi-modal medical image fusion. Over the years, Dr. Nair has taught a variety of computer science courses, such as Artificial Intelligence, Data Science, and Blockchain Technology, while actively contributing to curriculum development and department administration. Dr. Nair's research endeavors are well-recognized, with numerous publications in top-tier journals and conferences. Her work includes pioneering efforts in medical image fusion and cancer detection using deep learning frameworks. She has collaborated on projects addressing real-world problems, such as IoT-driven energy conservation and academic integrity using AI models. Notably, she has contributed to various book chapters and holds patents for innovative technological solutions. In addition to her scholarly achievements, Dr. Nair actively participates in faculty development programs and serves as a reviewer for several prestigious scientific journals, enhancing her contributions to the research community. Dr. Balamurugan Balusamy is currently working as an Associate Dean Student in Shiv Nadar Institution of Eminence, Delhi-NCR. He is part of the Top 2% Scientists Worldwide 2023 by Stanford University in the area of Data Science/AI/ML. He is also an Adjunct Professor in the Department of Computer Science and Information Engineering, Taylor University, Malaysia. His contributions focus on engineering education, blockchain, and data sciences

• Introduction: Foundations of Deep Learning for Healthcare Applications1. Magnitude-Based Neural Network Pruning for Medical Imaging2. Post-Training Quantization for Point-of-Care Diagnostic Systems3. Knowledge Distillation from Ensemble Models to Single-Shot Detectors4. Dynamic Neural Architecture Search for Adaptive Clinical Workloads5. Federated Learning with Differential Privacy in Multi-Hospital Networks6. Real-Time Inference Optimization for Critical Care Monitoring7. Edge-Cloud Hybrid Architectures for Telemedicine Applications8. Memory-Efficient Training Strategies for Medical Deep Learning9. Model Compression via Tensor Decomposition for Wearable Health Devices10. Automated Model Selection and Hyperparameter Optimization for Clinical AI11. Interpretable Lightweight Models for Clinical Decision Support12. Continuous Learning and Model Updates in Deployed Healthcare Systems13. Security and Adversarial Robustness in Lightweight Medical AI14. Regulatory Compliance and Validation Frameworks for Optimized Medical AI15. Case Studies and Implementation Blueprints for Healthcare AI Deployment16. Cost-Benefit and ROI Analysis of Lightweight AI in Global Healthcare Settings