DeepCPD: deep learning with vision transformer for colorectal polyp detection.

One of the most severe cancers worldwide is Colorectal Cancer (CRC), which has the third-highest incidence of cancer cases and the second-highest rate of cancer mortality. Early diagnosis and treatment are receiving much attention globally due to the increasing incidence and death rates. Colonoscopy is acknowledged as the gold standard for screening CRC. Despite early screening, doctors miss approximately 25% of polyps during a colonoscopy examination because the diagnosis varies from expert to expert. After a few years, this missing polyp may develop into cancer. This study is focused on addressing such diagnostic challenges, aiming to minimize the risk of misdiagnosis and enhance the overall accuracy of diagnostic procedures. Thus, we propose an efficient deep learning method, DeepCPD, combining transformer architecture and Linear Multihead Self-Attention (LMSA) mechanism with data augmentation to classify colonoscopy images into two categories: polyp versus non-polyp and hyperplastic versus adenoma based on the dataset. The experiments are conducted on four benchmark datasets: PolypsSet, CP-CHILD-A, CP-CHILD-B, and Kvasir V2. The proposed model demonstrated superior performance compared to the existing state-of-the-art methods with an accuracy above 98.05%, precision above 97.71%, and recall above 98.10%. Notably, the model exhibited a training time improvement of over 1.2x across all datasets. The strong performance of the recall metric shows the ability of DeepCPD to detect polyps by minimizing the false negative rate. These results indicate that this model can be used effectively to create a diagnostic tool with computer assistance that can be highly helpful to clinicians during the diagnosing process. [ABSTRACT FROM AUTHOR]