Abstract
Colorectal cancer (CRC) is a major global health concern. Its early diagnosis is extremely important, as it determines treatment options and strongly influences the length of survival. Histologic diagnosis can be made by pathologists based on images of tissues obtained from a colonoscopic biopsy. Convolutional neural networks (CNNs)-i.e., deep neural networks (DNNs) specifically adapted to image data-have been employed to effectively classify or locate tumors in many types of cancer. Colorectal histology images of 28 normal and 29 tumor samples were obtained from the National Cancer Center, South Korea, and cropped into 6806 normal and 3474 tumor images. We developed five modifications of the system from the Visual Geometry Group (VGG), the winning entry in the classification task in the 2014 ImageNet Large Scale Visual Recognition Competition (ILSVRC) and examined them in two experiments. In the first experiment, we determined the best modified VGG configuration for our partial dataset, resulting in accuracies of 82.50%, 87.50%, 87.50%, 91.40%, and 94.30%, respectively. In the second experiment, the best modified VGG configuration was applied to evaluate the performance of the CNN model. Subsequently, using the entire dataset on the modified VGG-E configuration, the highest results for accuracy, loss, sensitivity, and specificity, respectively, were 93.48%, 0.4385, 95.10%, and 92.76%, which equates to correctly classifying 294 normal images out of 309 and 667 tumor images out of 719.