CRCCN-Net: Automated framework for classification of colorectal tissue using histopathological images

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Colorectal cancer has a high mortality rate that continuously affects human life globally. Early detection of it extends human life and helps in preventing disease. Histopathological inspection is a frequently used approach to diagnose and detect colorectal cancer. Visual inspection of histopathological diagnosis requires more inspection time and the decision depends on the subjective perception of clinicians. This work proposed lightweight, less complex convolutional neural network-based architecture for automated classification of multi-class colorectal tissue histopathological images using two publicly available datasets, colorectal histology, and NCT-CRC-HE-100K, respectively. Histopathological images are provided as input to pre-trained models Xception, InceptionResNetV2, DenseNet121, VGG16, and the proposed network colorectal cancer classification convolutional neural network. This is the first study that compares the computational time of different deep learning architectures for the classification of colorectal tissue. The developed network requires less computational time for training compared to other pre-trained models. Accuracy, sensitivity, precision, false-positive rate, false-negative rate, specificity, F-1 score, and area under the curve have been used to evaluate the performance of the proposed architecture. The proposed network attained an accuracy of 93.50%, and 96.26% on the colorectal histology dataset, and NCT-CRC-HE-100K dataset, respectively. On the merged dataset, an accuracy of 99.21% is achieved by the newly developed network. The comparative analysis shows that the proposed framework outperformed existing state-of-the-art approaches. Clinicians may install the presented CRCCN-Net to confirm the diagnosis in the hospitals.