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Full Description
The volume explores a wide-range of applications of computer vision in the field of healthcare. Computer vision, an interdisciplinary field that combines image processing, pattern recognition, and artificial intelligence, has the potential to revolutionize healthcare by enabling automated analysis and interpretation of medical images and videos. This chapter will provide an overview of the current advancements and potential future directions of computer vision applications in healthcare. Specifically, the book examines the application of computer vision techniques in various healthcare domains, including diagnostic imaging, surgical interventions, clinical decision support, and healthcare management. It reviews the challenges and limitations in implementing computer vision systems in real-world healthcare settings and discusses potential solutions.
Overall, this book aims to shed light on the current and potential applications of computer vision in healthcare. It will summarize the state-of-the-art techniques, their benefits, challenges, and implications, while also discussing potential ethical considerations. The objective is to provide healthcare professionals, researchers, and practitioners with an understanding of the transformative impact of computer vision in the healthcare sector and inspire further research and development in this exciting field.
Contents
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.
