ResNet110 and Mask Recurrent Convolutional Neural Network Based Detection and Classification of Colorectal Cancer

Globally, Colorectal Cancer (CRC) is the one most significant cancer types as well as it grows in a region of colon of large intestine. An early CRC detection is supportive to handle the impression of accumulating cancer cells. However, the detection process of utilizing the colon images is expensive as well as time-consuming. Recently, Deep Learning (DL)-based approaches are developed for the CRC detection. Therefore, this research proposes the efficient pre-trained architecture for the detection and classification of CRC. Initially, the benchmark dataset of Warwick-QU is collected for the estimation of the effectiveness of the proposed method. Then, the pre-processing is done by the utilization of Noise removal as well as Contrast Enhancement. Then, pre-processed data is to extracted by the utilization the pre-trained architecture. Finally, the outcome of extracted features is classified by the utilization of improved Mask Recurrent-Convolutional Neural Network (Mask R-CNN). The effectiveness of the proposed method is validated by various performance metrices and it achieves the accuracy of 99.67%, specificity of 99.56%, sensitivity of 99.57%, precision of 97.36% and F1-score of 98.34% when compared to the existing methods such as ResNet-50, Stacked Gated Recurrent Unit (SGRU) and Mobile Net.