Deep learning

Radiology

Geology

Pattern recognition (psychology)

Erosion

Machine learning

Paleontology

Artificial intelligence

Capsule endoscopy

Computer science

Receiver operating characteristic

Computer vision

Medicine

Convolutional neural network

Physics in Medicine and Biology

A novel computer-aided detection method based on deep learning framework was proposed to detect small intestinal ulcer and erosion in wireless capsule endoscopy (WCE) images. To the best of our knowledge, this is the first time that deep learning framework has been exploited on automated ulcer and erosion detection in WCE images. Compared with the traditional detection method, deep learning framework can produce image features directly from the data and increase recognition accuracy as well as efficiency, especially for big data. The developed method included image cropping and image compression. The AlexNet convolutional neural network was trained to the database with tens of thousands of WCE images to differentiate lesion and normal tissue. The results of ulcer and erosion detection reached a high accuracy of 95.16% and 95.34%, sensitivity of 96.80% and 93.67%, and specificity of 94.79% and 95.98%, correspondingly. The area under the receiver operating characteristic curve was over 0.98 in both of the networks. The promising results indicate that the proposed method has the potential to work in tandem with doctors to efficiently detect intestinal ulcer and erosion.

Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images