abstract
- © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Colorectal cancer cases have been increasing at an alarming rate each year, imposing a healthcare burden worldwide. Multiple efforts have been made to treat this malignancy. However, early screening has been the most promising solution. Optical endoscopy is the primary diagnosis and treatment tool for these malignancies. Even though its success, the endoscopic process represents a challenge due to noisy data, a limited field of view and the presence of multiple artefacts. In this work, we present a comparison between two real-time deep learning frameworks trained to detect artefacts in endoscopic data. Both networks are trained using different data augmentation techniques to analyze their effect when the models are evaluated using data coming from a different distribution. We evaluated these models using the mean average precision (mAP) evaluation metric at a different intersection over union values. Both models outperformed state-of-the-art methods that were evaluated using the same dataset. Also, the use of data augmentation techniques showed an overall improvement in terms of mAP when compared to the case in which no augmentation was applied.