An optimized bidirectional vision transformer based colorectal cancer detection using histopathological images

The high death rate of colorectal cancer has a long-term impact on human life around the world. Early detection of colorectal cancer leads to an increase in the survival rate of the patients. Overfitting issues occurred due to the presence of imbalanced datasets. To resolve the challenges, a novel deep learning-based approach based on an effective optimization strategy with a bidirectional vision transformer model for colorectal cancer detection. Disease classification is performed using two datasets: colorectal histology images and the NCT-CRC-HE-100 K dataset. Initially, the histopathological images from the dataset are trained and pre-processed using a Trimmed Pixel density-based median filter (TPDMF), which removes noise from the input images and enhances the quality of the images. The features of the pre-processed image are extracted using the Capsule Assisted Res2Net (CAR2N) model, and an optimized Bidirectional Vision Transformer (OBVT) model is presented to identify colorectal cancer. Here, the Bi- Long short-term memory (Bi-LSTM) model is used in the Multi-layer perception module of the vision transformer to reduce the complexity of the detection process. The proposed methodology uses a chaotic sequence-based Snake optimization algorithm (Ch-SOA) to tune the hyperparameters in the proposed model. The proposed model can be analyzed using different performance metrics like accuracy, precision, sensitivity, specificity, False negative rate (FNR), and False positive rate (FPR). The proposed model can obtain an accuracy of 97.11458 % for the colorectal histology images dataset and 97.01235 % for the NCT-CRC-HE-100 K dataset. From this analysis, the proposed model can obtain better results than other existing models.