The technological advancements made in recent decades have not only helped us better comprehend the morphology and physiology of the organs of the human body, but they have also advanced the diagnosis and, therefore, the treatment of a number of diseases in a variety of medical specialties from very early stages. Artificial Intelligence (AI) and Computer Vision (CV) enable us to collect, process, interpret, and analyze a limitless quantity of static and dynamic medical data in real time, which improve the way each disease is characterized and the patients are chosen. Many potentially fatal illnesses, such as COVID-19, pneumonia, and cancer, can be cured if diagnosed in initial stages very early on. Computer-based medical imaging techniques, such as CT scan and X-rays are useful in detecting all of these illnesses. On the other hand, various brain anomalies and heart diseases can also be anticipated using biological signals, like electroencephalography (EEG), electrocardiogram (ECG) etc. The application of machine learning makes the predictions more accurate and help the clinician to detect appropriate one. This helps in faster recognition of disease as well as with the intervention of the technology, makes it feasible to spread to the remote places. The goal of the book is to create machine learning algorithms that aids in the analysis of diverse medical data and the prediction of diseases based on the characteristics of the data.
Advancements in AI and Computer Vision are revolutionizing medical diagnostics by enabling real-time analysis of vast data. This book focuses on ML algorithms that analyze medical data and predict diseases based on key features.
1. Machine Learning Approaches for Disease Diagnosis Cervical Cancer
Disease Prediction, A Case Study.
2. A Conjunctive Framework of Ensemble
Learning Model with Explainable AI for Optimizing Parametric Eminence in
Heart Disease Prediction.
3. AI for Early Identification of Down Syndrome
Patients.
4. Advanced Biomedical Signal Decomposition and Denoising by
Integrating Traditional and Machine Learning Techniques.
5. Enhanced
Retinopathy Detection Using Nested U-Net for Red Lesion Segmentation in
Retinal Fundus Images.
6. EDiNA-UNet for Liver Segmentation from CT Images.
7. Self-Supervised Patch Contrastive Learning for Efficient Tumour Detection
in Histopathology Images with Minimal Annotations.
8. A Comprehensive
Analysis of Personalized Treatment using Digital Twin Technology in
Healthcare.
9. NIC Health: Nature Inspired Computing for Secure and
Intelligent Healthcare Systems.
10. Semantic Web for Addressing Data
Integration Challenges: Semantic Data Fabric for Healthcare.
11. Personalized
Medicine Prediction in Homeopathy.
Sriparna Saha (M.E. & Ph.D, JU) is currently an Assistant Professor (Stage-II) in the Department of Computer Science and Engineering of Maulana Abul Kalam Azad University of Technology, West Bengal, India. She has more than 12 years of experience in teaching and research. Her research area includes AI, CV, HCI etc. with over 90 publications in international journals and conferences. Her major research proposal is accepted for Start Up Grant under UGC Basic Scientific Research Grant.
Lidia Ghosh (Gold-Medalist, M.Tech., JU) is an Assistant Professor in the Department of Computer Application at the RCC Institute of Information Technology, India. She was a Postdoctoral Fellow at Liverpool Hope University, UK, and has received multiple prestigious fellowships, including the Rashtriya Uchchatara Shiksha Abhiyan Doctoral Fellowship. She has published over 50 research papers and serves as a reviewer for top IEEE journals. Her research focuses on Cognitive Neuroscience, Deep Learning, Type-2 Fuzzy Sets, and Human Memory Formation.
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