Effectiveness of encoder-decoder deep learning approach for colorectal polyp segmentation in colonoscopy images

Colorectal cancer is considered one of the deadliest diseases, contributing to an alarming increase in annual deaths worldwide, with colorectal polyps recognized as precursors to this malignancy. Early and accurate detection of these polyps is crucial for reducing the mortality rate of colorectal cancer. However, the manual detection of polyps is a time-consuming process and requires the expertise of trained medical professionals. Moreover, it often misses polyps due to their varied size, color, and texture. Computer-aided diagnosis systems offer potential improvements, but they often struggle with precision in complex visual environments. This study presents an enhanced deep learning approach using encoder-decoder architecture for colorectal polyp segmentation to capture and utilize complex feature representations. Our approach introduces an enhanced dual attention mechanism, combining spatial and channel-wise attention to focus precisely on critical features. Channel-wise attention, implemented via an optimized Squeeze-and-Excitation (S&E) block, allows the network to capture comprehensive contextual information and interrelationships among different channels, ensuring a more refined feature selection process. The experimental results showed that the proposed model achieved a mean Intersection over Union (IoU) of 0.9054 and 0.9277, a dice coefficient of 0.9006 and 0.9128, a precision of 0.8985 and 0.9517, a recall of 0.9190 and 0.9094, and an accuracy of 0.9806 and 0.9907 on the Kvasir-SEG and CVC-ClinicDB datasets, respectively. Moreover, the proposed model outperforms the existing state-of-the-art resulting in improved patient outcomes with the potential to enhance the early detection of colorectal polyps.