In pathological practice, a substantial number of procedures are followed to detect and analyze the disease in humans. Usually, pathologists examine the suspicion to diseases in various examination levels ranging from tissues to organs to discover the cause and the stage of the disease. The proposed study aims to investigate the endoscopy recorded digital pictures of Gastric Polyps (GP). This study aims to implement a Computer based Disease Examination Tool (CDET) to analyze the abnormal regions in the stomach. The proposed work comprises a threshold process based on the Brain-Strom-Optimization-Algorithm and Kapur’s Function (BSOA+KF) to augment the polyp fragment and the segmentation based on the Active-Contour (AC) to mine the polyp segment. The performance of implemented technique is checked using the benchmark GP endoscopy images of CVC-ClinicDB dataset. The performance of the proposed CDET is confirmed based on a relative assesment with the Ground-Truth images existing in the considered database. Further, the performance of the AC segmentation is validated with Chan-Vese (LAC) and Seed-Region-Growing (SRG) segmentation techniques. The results of this study confirms that, AC segmentation technique offers better performance values compared to LAC and SRG.