Neural Networks for Classification of Eye Conjunctivitis in Telehealth: a Conceptual Architecture

Abstract

Telehealth systems have developed rapidly into more conventional ways that can provide medical assistance, especially for people in remote areas. Despite rapid technological and practical developments, there are still many knowledge gaps regarding the effective use of telemedicine. Annually, nearly 1-4% of the general population might experience conjunctivitis. This study is focused on an experimental design for the classification of degrees of severity in colour medical images in telemedicine, in particular red as one of the key symptoms in the diagnosis of various pathologies. The quality of digital images is a pivotal thing in terms of telemedicine for accurate diagnosis because degraded or distorted colours can lead to errors. This study focused on the use of digital images in teleconsultation, in particular images displaying conjunctivitis (red eyes) as a case study since this pathology integrates red in its diagnosis. The deep self-organising map is suggested to be applied to classify the different severities. Moreover, U-Net, a deep learning network, is proposed to employ the segmentation of eye images for better feature extraction. Although this approach is focused on the problem of red eye image classification, it can be extended in the future to also be applied to other pathologies.