Main description:

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases.

The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer's disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

Contents:

1. Introduction. 2. Machine Learning for Signal Analysis. 3. Deep Learning for Cancer Detection. 4. Deep Learning for Diabetic Cases. 5. Deep Learning for Blood Sample Images. 6. Deep Learning for Skin Image Analysis. 7. Deep Learning for Alzheimer's Diseases Detection. 8. Deep Leaning for Coronary Disease Detection. 9. Deep Learning for Medical Image Forensic. 10. Deep Learning for Fetal Anomaly Detection. 11. Digital Detectors in Medicine. 12. Deep Learning for Plant Phytology.