An Endoscopic Lesion Segmentation Improvement

Gastrointestinal cancer is one of the deadliest diseases all over the world. In medical imaging technology, cancer diagnosis has been evolving, especially endoscopes and currently applying artificial intelligence and deep learning on improving endoscopist’s imaging abilities. By using acquisition of images of tissues and organs, physicians are able to diagnose cancer in gastrointestinal track, besides of some limitations as depending on quality of endoscopy, experience of physicians or time consuming. With the development of technology, applications of deep learning are more effective in improving imaging techniques and diagnosis. Due to the state-of-the-art of deep learning, we applied VGG16, DenseNet201, HarDNet MSEG and HarDNet MSEG with attention to obtain some better results. Our results provide the comparison with mDice, mIoU and inference time on some of our trained models on the CVC-Clinic DB and Kvasir-SEG dataset. The best obtained mDice and mIoU are over 0.9 and 0.8, respectively.