Neural Pathways to Health: Deep Learning Applications in Medical Science

Machine Learning algorithms proved to be very helpful in medical science in the presence of enormous amounts of data. Particularly, it analyzes medical data, such as images, genetic information, and patient records, aiding healthcare professionals in faster and more accurate diagnoses. Deep learning has gained significance in automated report generation, expediting the diagnostic process and improving overall accuracy for more effective treatment plans. Specifically, applications like interpreting chest x-ray images highlight the need for precision in the absence of practitioners. To ensure trustworthiness and reliability, incorporating explainability concepts is crucial.
Machine Learning customizes medical interventions based on individual patient characteristics, considering factors like genetics, lifestyle, and health history. This personalized approach enhances treatment effectiveness, minimizes side effects, and boosts patient satisfaction. In the realm of dermatopathology research, interpretable deep learning techniques are applied to analyze histological images of common skin cancers such as intraepidermal carcinoma (IEC), squamous cell carcinoma (SCC), and basal cell carcinoma (BCC). The study involves classifying skin tissue into 12 dermatological classes, including structures like sweat glands and hair follicles, showcasing the potential of automatic machine analysis in this field.

Faculty Supervisor:

Sabah Mohammed;Saad Ahmed

Student:

Partner:

National University of Sciences and Technology

Discipline:

Computer science

Sector:

Artificial Intelligence; Health and Related Sciences & Technology

University:

Lakehead University

Program: