Colorectal cancer detection based on convolutional neural networks (CNN) and ranking algorithm

Abstract

With the development of targeted therapies, many treatments are based on molecular studies, which require sampling tumor tissue from paraffin blocks for sequencing. An automated solution could potentially reduce the workload of pathologists by acting as a screening device and may reduce the subjectivity in diagnosis. In tissue-based diagnostics, most of the work still needs to be done manually by a pathologist using a microscope to examine stained slides. The foundation of such tasks is to accurately distinguish cancer/malignant cells from normal/benign cells. However, the determination of tumor content is poorly reproducible with significant variation. As the size of tumor regions can be very small, pathologists are often required to use high magnification for detecting tumor cells. This requirement significantly increases the workload for pathologists. As digital pathology datasets have become publicly available and have opened up the possibility of evaluating the feasibility of applying deep learning techniques to improving the efficiency and quality of histologic diagnosis. The model proposed in this work is an application to detect colorectal cancer based on the Convolutional Neural Network and Ranking algorithm. Based on the performance evaluation, it is found that the proposed model is yielding better results in diagnosis of Colorectal Cancer than the existing methods in terms of Recall, Precision and Accuracy.