Enhancing Diagnostic Precision for Early Detection of Gastric Polyps with U-Net3+ Segmentation

Digestive disorders have become very rampant these days and some of these disorders which include gastritis may cause rise in stomach polyps. It is therefore important to identify these precancerous polyps early in the disease process so that treatment can be taken to ensure they do not grow into cancer. Specifically, in the context of semantic segmentation, there has been growing concern on using deep learning methods. Among them, several segmentation models that have been introduced here have been used in various medical image segmentation tasks. With respect to our research, we investigate semantic segmentation using the U-Net3+ architecture on Kvasir-Sessile, containing 1,200 high resolution endoscopic images of sessile polyps; a diverse set of polyps that challenge segmentation due to their subtle and diverse appearance. We applied U-Net3+ to perform the segmentation of sessile polyps in the Kvasir-Sessile dataset. The increase in the ROC AUC to 0.987, MCC of 0.998, and F1-score to 0.977 from the experiments indicate that our proposed method reaches higher accurate in segmenting sessile polyps from complex endoscopic background. In addition, through qualitative analysis of the segmented images, histogram intersection, Hausdorff distance, and MSE values supported by the visual inspection confirmed precise and accurate segmentation of the various shapes and sizes of polyps.