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% .
Keywords: Colon cancer, CNN, H&E stained histopathology, ResNet50.