Gastrointestinal Tract Anomaly Detection from Endoscopic Videos Using Object Detection Approach

Endoscopy is a medical procedure used for the imaging and examination of our internal body organs for detecting, visualizing and localising anomalies to facilitate their further treatment. Currently, the medical practitioners expertise is vastly relied upon to analyse these endoscopic videos. This can be a bottleneck in rural areas where specialized medical practitioners are scarce. By learning from and improving upon existing research, the proposed system leverages object detection methods to achieve an automated detection mechanism to provide real-time annotations to assist medical professionals performing endoscopy and provide insights for educational purposes. It works by extracting video frames and processing it using a real-time object detection deep learning model trained on a standard dataset to detect two anomalies namely: Esophagitis and Polyps. The output is in the form of an annotated video. Using Intersection over Union metric (IOU), the model is observed to be performing accurately on the training set but shows a lesser accuracy on the test set of images. This however can be improved using alternate metrics which are more suited to irregular shaped multi-class, multiple object detection and can better explain the observed results.