Deep convolutional neural network-based automated lesion detection in wireless capsule endoscopy
Abstract
Because most of the capsule-endoscopic images contain normal mucous membranes, physicians spend most of their reading time observing normal areas. Thus, a significant reduction in their reading time would be possible if only the portion of the image frame for which a particular lesion is suspected can be read intensively. This study aims to develop a deep convolutional neural-network-based model capable of automatically detecting lesions in the capsule-endoscopic images of a small bowel. The proposed model consists of two deep neural networks in parallel, each of which takes in images in RGB and CIELab color spaces, respectively. The neural-networks model is based on transfer-learned GoogLeNet architecture. Our proposed algorithm showed promising results in classifying endoscopic images where lesions exist (98.56% accuracy). If the proposed algorithm is used to screen abnormal images, it is expected to reduce a physician's reading time and to improve his/her reading accuracy.
- Publication:
-
International Forum on Medical Imaging in Asia 2019
- Pub Date:
- March 2019
- DOI:
- 10.1117/12.2522159
- Bibcode:
- 2019SPIE11050E..1NJ