Monkeypox Detection Using Computer Vision

Student Name: Samyak Jain
Defense Date:
Location: Eaton Hall, Room 2001B
Chair: Prasad Kulkarni

David Johnson, (Co-Chair)

Hongyang Sun


As the world recovers from the damage caused by the spread of COVID-19, the monkeypox virus poses a new threat of becoming a global pandemic. The monkeypox virus itself is not as deadly or contagious as COVID-19, but many countries report new patient cases every day. So it wouldn't be surprising if the world faces another pandemic due to lack of proper precautions. Recently, deep learning has shown great potential in image-based diagnostics, such as cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, since monkeypox has infected human skin, a similar application can be employed in diagnosing monkeypox-related diseases, and this image can be captured and used for further disease diagnosis. This project presents a deep learning approach for detecting monkeypox disease from skin lesion images. Several pre-trained deep learning models, such as ResNet50 and Mobilenet, are deployed on the dataset to classify monkeypox and other diseases.

Degree: MS Project Defense (CS)
Degree Type: MS Project Defense
Degree Field: Computer Science