Automated Colorectal Polyps Detection from Endoscopic Images using MultiResUNet Framework with Attention Guided Segmentation

The early detection of colorectal polyps is crucial for the reduction of mortality rates. However, manually identifying polyps is time-consuming and expensive, increasing the risk of missing them. Our paper aims to address this issue by presenting an automated segmentation approach for colorectal polyps. This paper proposes a method that combines a skip connection with hybrid attention guidance (AG) using attention guidance (AG) and residual path frameworks to identify salient features. Furthermore, we augment test samples using original, horizontal flip, and vertical flip transformations to enhance model robustness through Test Time Augmentation (TTA). The model was trained with Kvasir-seg samples and evaluated on Kvasir-seg and CVC-ClinicDB datasets to gauge generalizability. A significant accuracy (0.9546), a Dice Similarity Coefficient (DSC) of 0.8557, a Cross-section over Union (IoU) of 0.8824, a Recall (0.8221), a Precision (0.8922), an area under Receiver Operating Characteristics (ROC-AUC) of 0.9454, and an area under Precision-Recall (AUC-PR) of 0.8717 were achieved without TTA. Through TTA integration, accuracy (0.9993), DSC (0.8663), IoU (0.8277), Recall (0.8060), Precision (0.9364), and ROC-AUC (0.9587) have been improved. A comparison of our framework with state-of-the-art models demonstrated its effectiveness and segmentation capabilities. Additionally, the proposed model contains only 0.47 million parameters and a weight size of 6.71 MB, illustrating its potential for clinical diagnostics. A computer-aided diagnosis (CAD) system improves patient outcomes by detecting colorectal polyps early and improving segmentation accuracy.