Assessment of a deep-learning system for colorectal cancer diagnosis using histopathology images
Purna Kar, Sareh Rowlands · Research Square · 2022
Abstract Colorectal Cancer is the one of the most common forms of cancer hence, an early and accurate detection is crucial. Manual diagnosis is a tedious and time-consuming job which is prone to human errors as it involves visual examinations of pathological images. Therefore, it is imperative to use computer-aided detection (CAD) systems to interpret the medical images for a quicker and more accurate diagnosis. The traditional methods for diagnosis comprise extraction of features based on texture, pattern, illumination etc. from pathological images and then use these features in a Machine Learning model for binary classification i.e., cancerous, or non-cancerous. Deep-learning approaches like the Convolutional neural networks (CNNs) have proved to be very effective in classifying and predicting cancer from pathological images. In this study, we have assessed several CNN-based techniques for cancer diagnosis on digitized histopathology images. We have also compared the results of traditional methods for diagnosis with the deep-learning models. Moreover, we have proposed a new model by borrowing the idea from Xception architecture (Xception+), which outperforms the existing architectures. Furthermore, we have studied the effect of transfer learning technique by using models pre-trained on unrelated histopathology images.