Automated Strabismus Detection for Telemedicine Applications
This work addresses timely strabismus detection for telemedicine applications, but it appears incremental as it applies existing deep learning methods to a new dataset.
The authors tackled automated strabismus detection for telemedicine by proposing RF-CNN, an end-to-end framework that segments eye regions and classifies them with deep neural networks, achieving good performance on their established tele strabismus dataset.
Strabismus is one of the most influential ophthalmologic diseases in human's life. Timely detection of strabismus contributes to its prognosis and treatment. Telemedicine, which has great potential to alleviate the growing demand of the diagnosis of ophthalmologic diseases, is an effective method to achieve timely strabismus detection. In this paper, a tele strabismus dataset is established by the ophthalmologists. Then an end-to-end framework named as RF-CNN is proposed to achieve automated strabismus detection on the established tele strabismus dataset. RF-CNN first performs eye region segmentation on each individual image, and further classifies the segmented eye regions with deep neural networks. The experimental results on the established tele strabismus dataset demonstrates that the proposed RF-CNN can have a good performance on automated strabismus detection for telemedicine application. Code is made publicly available at: https://github.com/jieWeiLu/Strabismus-Detection-for-Telemedicine-Application.