Identifying and classifying patient-uploaded image quality for teledermatology practices

This project focuses on the development of an automated system to detect images consisting of skin moles. The detection of images consisting of moles will play a central role in dermatology screening using telemedical and virtual healthcare platforms. Upon classification, images consisting of moles will be sent to specialists for further diagnosis and treatment procedures. To achieve the proposed objective, this project will utilize the recent developments in image processing, computer vision, machine learning, and artificial intelligence techniques. The proposed project will be undertaken by an MSc student who will develop image processing approaches to enhance the image quality before proceeding with the development of the feature extraction approach. Once the salient features are extracted from the images, the intern will develop a machine learning approach to automatically detect and classify images consisting of skin moles. This project will be undertaken in collaboration with ORO Health Inc.

Faculty Supervisor:

Kumaradevan Punithakumar

Student:

Partner:

ORO Health Inc

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program: