Deep Learning-Based Colon Cancer Tumor Prediction Using Histopathological Images

Abstract

Colorectal cancer is one of the deadliest diseases and one of the most difficult diseases to diagnose. A big reason for this is that it takes a long time to identify at an early stage. For treatment, a rapid and precise diagnosis of nodules is very crucial. In order to identify cancer in its early stages, a variety of techniques have been employed. Deep learning approaches were used in this work in order to identify Colorectal cancer tumors. In our this research, we used a dataset of same dimension of colon cancer tissues histopathological images. We proposed a deep learning model for predicting CRC tumors from histopathological images. CNN technique used for analyzing complex data. By CNN technique we analyzed our complex tumor images for identifying abnormal or suspicious tumor patterns. We made a five-layer deep neural network model. It consists of the input layer, four hidden layers, and the output layer. We used Rectified linear unit (ReLU) activation function in the hidden layer and the Softmax function in the output layer. We obtained an accuracy 99.70% from our deep learning model and our model loss was 0.0160. We calculate precision, recall, and F-score for the performance evaluation of our method. It is evident from our experiment that our proposed model produces a better result than some related works.