Date of Award

9-15-2022

Publication Type

Thesis

Degree Name

M.Sc.

Department

Computer Science

Keywords

Deep Learning;Residual Learning;Residual Networks;Transfer Learning;Whole Slide Images

Supervisor

Boubakeur Boufama

Abstract

Colorectal cancer (CRC) is an emerging global health concern. An average of 73 Canadians will be diagnosed with CRC every day and 27 Canadians will lose their life as a result of it. CRC accounts for 12% of all cancer deaths in Canada in the year 2020. Early and accurate diagnosis is vital in saving lives as it significantly influences the length of survival of the patient. Deep learning can be leveraged to aid in the task of identifying cancerous cells within pre-cancerous tissue samples, which are taken from colorectal polyps of patients for CRC screening. In this study, an attempt to improve existing supervised methods of classification of colorectal cancer is made. By revamping/improving the deep learning architecture in ResNet. The network will be trained on a much larger and relevant dataset of colorectal WSI (Whole Slide Image) patches. This study aims to attain better overall accuracy by incorporating color features, which have not been concentrated on in previous studies. All while retaining similar performance as compared to existing state-of-the-art methods of CRC classification. Four network models are applied to a large histopathological dataset. All network models are variations of Residual networks at multiple depths. The best results are attained using a pre-trained ResNet-50 model. The overall results show that the residual network performs similarly to the much deeper DenseNet-121 model and better than the cell level framework described in a previous study. The ResNet-50 model achieved 88.58%, 92.04%, 81.86%, 86.65% Accuracy, Precision, Recall and F1-Score respectively.

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