Colon Cancer Nuclei Classification with Convolutional Neural Networks

CRC or Colorectal Cancer also called as bowel cancer is the development of cancer from colon or rectum. Detection of CRC can be very essential that can help the diagnosed with effectual treatment preventing potential loss of life. Conventional methods can be challenging because of its excessive dependence on the expert to detect accurately. This paper aims to compare results obtained from popular deep learning models such as AlexNet, GoogleNet, MobileNet, by performing on the “CRCHistoPhenotypes” dataset furthermore, inter-comparison of the same is done by applying Data Augmentation methods. Comparison is done on the basis of training time, accuracy, weighted f1 score, specificity and sensitivity. An enhancement in testing accuracy was observed, even in the case of the state-of-the-art network, GoogLeNet. It exhibited an increase of around 2.3%, achieving an impressive 80% accuracy following the utilization of data augmentation methods.