Classification Tool to Predict the Presence of Colon Cancer Using Histopathology Images

Abstract

The proposed model compares the efficiency of CNN and ResNet50 in the field of digital pathology images. Deep learning methods are widely used in all fields of disease detection, diagnosis, segmentation, and classification. CNN is the widely used image classification algorithm. But it may show less accuracy in case of complex structures like pathology images. Residual Networks are a good choice for pathology image classification because the morphology of digital pathology images is very difficult to distinguish. Colon cancer is one of the common cancers, and it is one of the fatal diseases. If early-stage detection has been done using biopsy results, it will decrease the mortality rate. ResNet50 is selected among the variants as its computational complexity is moderate and provides high accuracy in classification as compared to others. The accuracy metric used here is the training and validation accuracy and loss. The training and validation accuracy of ResNet50 is 89.1% and 90.62%, respectively, whereas the training loss and validation loss are 26.7% and 24.33%, respectively. At the same time, for CNN, the accuracy is 84.82% and 78.12% and the loss is 36.51% and 47.33% .