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An Ensemble Approach for Automatic Artefact Detection on Gastroendoscopy Images

Endoscopy imaging is a clinical procedure for the early detection of numerous cancers as well as for therapeutic procedures and minimally invasive surgery. Using endoscopic examination data to detect diseases is very helpful for medical doctors and speeds up the diagnosis. Because of the very narrow area, captured frames during endoscopic examination include a variety of artefacts. Artefacts degrade diagnostic image quality, which in turn makes disease diagnosis difficult for both clinicians and computer aided disease detection algorithms. Therefore, it is very crucial to find and eliminate those artefacts from medical images. In this paper, a detection system which utilizes ensemble of deep learning models and data augmentation is proposed. A fast and accurate object detection model which is YOLOv5 (improved version of YOLOv4) is selected as a base model. The 3 separate models are trained with segregated and augmented data; then, the models are combined to make an ensemble. The EndoCV2020 dataset is utilized to benchmark the ensemble model. The model achieves state-of-the-art performance with 49.6 mAP. The final mAP is calculated averaging several APs for different IoU thresholds (starting from 0.25 IoU to 0.75 Iou with step size 0.05).