Full Description
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.
Contents
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.
