Colorectal Disease Classification (CDC)

Cancer is the leading cause of death in Canada, and early diagnostics significantly increase the chances of treatment and survival. However, this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis (CAD) systems have shown improving diagnostic accuracy. However, researchers need to further study to improve the accuracy of CAD tools. This study develops a computational approach for colon cancer classification based on deep convolution neural networks (CNN). I use hematoxylin and eosin-stained dataset collected from the Chaoyang Hospital. This approach contains two deep CNN architectures from the computer vision domain for representational learning of cancer classes. I compare the results of the two CNN architectures, ResNet50 and ResNeXt50, and their performance on the Chaoyang dataset. By training, the ResNeXt achieves the state-of-the-art result of 86.21%.