Deep Learning-Enabled Optimization of Polyp Frame Detection and Segmentation

Polyp segmentation is a method for identifying and characterizing polyps, or abnormal tissue development, as seen in frame images from medical procedures such as endoscopic or colonoscopy frame images. Polyp segments are reliant on high-quality imaging data, which can be intermittently accessible, causing lower detection and precision in segmentation. To overcome these challenges, we propose a novel deep learning (DL) method called intelligent chameleon swarm optimized-adaptive fully convolutional neural network (ICSO-AFConvNet) for polyp segmentation and frame detection. Initially, we gather the dataset named (KVasir-SEG), which contains a thousand frame images, and then contrast limited adaptive histogram equalization (CLAHE) technique, is used to preprocess the data. Experimental findings on the KVasir-SEG dataset reveal that ICSO-AFConvNet outperforms previous approaches in measures such as Dice, Interception over union (IoU), Recall and Precision. These findings show that the suggested ICSO-AFConvNET technique has tough generalization power and learning ability, making it an appealing alternative for practical applications with significant data variances.