This paper addresses the challenge of identifying and segmenting polypoid lesions in Wireless Capsule Endoscopy (WCE) images, a critical aspect of gastrointestinal (GI) tract disorder management. The study evaluates various computer-aided approaches to polyp detection using WCE imagery and focuses on the performance of the YOLO-V8 deep learning model. YOLO-V8, an advanced iteration of the YOLO series, is chosen for its superior accuracy and efficiency. The research utilizes the KID Dataset, a comprehensive repository of annotated WCE images, to train and evaluate the model. The evaluation metrics include precision, recall, and Dice score, which are used to assess the model's performance in segmenting polypoid lesions. The results show that YOLO-V8 m, one of the five versions of YOLO-V8, achieves high precision (98%) and recall (97.9%), outperforming other state-of-the-art models. The study highlights the potential of automated detection systems in improving GI polyp identification, emphasizing the importance of early detection and prevention in reducing cancer-related mortality. The research also underscores the need for more comprehensive datasets and further investigations into real-time capabilities and dataset adaptability.This paper addresses the challenge of identifying and segmenting polypoid lesions in Wireless Capsule Endoscopy (WCE) images, a critical aspect of gastrointestinal (GI) tract disorder management. The study evaluates various computer-aided approaches to polyp detection using WCE imagery and focuses on the performance of the YOLO-V8 deep learning model. YOLO-V8, an advanced iteration of the YOLO series, is chosen for its superior accuracy and efficiency. The research utilizes the KID Dataset, a comprehensive repository of annotated WCE images, to train and evaluate the model. The evaluation metrics include precision, recall, and Dice score, which are used to assess the model's performance in segmenting polypoid lesions. The results show that YOLO-V8 m, one of the five versions of YOLO-V8, achieves high precision (98%) and recall (97.9%), outperforming other state-of-the-art models. The study highlights the potential of automated detection systems in improving GI polyp identification, emphasizing the importance of early detection and prevention in reducing cancer-related mortality. The research also underscores the need for more comprehensive datasets and further investigations into real-time capabilities and dataset adaptability.