Polyp Segmentation Using a Hybrid Vision Transformer and a Hybrid Loss Function

Abstract

Accurate and early detection of precursor adenomatous polyps and their removal at the early stage can significantly decrease the mortality rate and the occurrence of the disease since most colorectal cancer evolve from adenomatous polyps. However, accurate detection and segmentation of the polyps by doctors are difficult mainly these factors: (i) quality of the screening of the polyps with colonoscopy depends on the imaging quality and the experience of the doctors; (ii) visual inspection by doctors is time-consuming, burdensome, and tiring; (iii) prolonged visual inspections can lead to polyps being missed even when the physician is experienced. To overcome these problems, computer-aided methods have been proposed. However, they have some disadvantages or limitations. Therefore, in this work, a new architecture based on residual transformer layers has been designed and used for polyp segmentation. In the proposed segmentation, both high-level semantic features and low-level spatial features have been utilized. Also, a novel hybrid loss function has been proposed. The loss function designed with focal Tversky loss, binary cross-entropy, and Jaccard index reduces image-wise and pixel-wise differences as well as improves regional consistencies. Experimental works have indicated the effectiveness of the proposed approach in terms of dice similarity (0.9048), recall (0.9041), precision (0.9057), and F2 score (0.8993). Comparisons with the state-of-the-art methods have shown its better performance.