Deep learning-enabled classification of gastric ulcers from wireless capsule endoscopic images

The detection of gastric ulcers is commonly carried out during clinical interventions. It poses many challenges, such as extended time of diagnosis, requirement for clinical expertise, and background noise elimination, especially in early ulcer detection. We adopt a transfer learning-based approach to automatically detect ulcers from wireless-capsule endoscopic (WCE) images. We employed DenseNet121, a convolutional neural network-based pre-trained model for ulcer classification, and realized an optimal performance matrix for test data after training and validation. The DenseNet121 pre-trained model achieved 99.94% classification accuracy with 100% precision, 97.67% recall, and 98.82% F1-score for the test dataset that demonstrates the efficacy of the adopted deep learning model for fast and accurate gastric ulcer screening.