The rapid technological advancements in the healthcare industry over recent decades have been transformative. These innovations have not only enhanced our understanding of the morphology and physiology of various organs but have also significantly improved the early diagnosis and treatment of numerous diseases across different medical specialties. This progress has been largely driven by advancements in artificial intelligence (AI) and computer vision (CV). AI and CV enable the real-time collection, processing, interpretation, and analysis of vast amounts of static and dynamic medical data, revolutionizing disease characterization and patient selection. Early detection is crucial in treating life-threatening illnesses such as COVID-19, pneumonia, and cancer. Computer-based medical imaging techniques, including CT scans and X-rays, play a vital role in diagnosing these conditions. Similarly, biological signals like electroencephalography (EEG) and electrocardiography (ECG) help anticipate brain anomalies and heart diseases. Machine learning further enhances the accuracy of disease prediction, assisting clinicians in making precise diagnoses. By facilitating faster disease recognition, these technologies also enable wider access to healthcare, including remote and underserved areas. This book aims to develop machine learning algorithms that analyze diverse medical data and predict diseases based on their characteristics, ultimately advancing healthcare diagnostics and treatment strategies.

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.