Automated detection of mild and multi-class diabetic eye diseases using deep learning
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(2020) 8:32 Sarki et al. Health Inf Sci Syst https://doi.org/10.1007/s13755-020-00125-5
RESEARCH
Automated detection of mild and multi‑class diabetic eye diseases using deep learning Rubina Sarki* , Khandakar Ahmed, Hua Wang and Yanchun Zhang
Abstract Diabetic eye disease is a collection of ocular problems that affect patients with diabetes. Thus, timely screening enhances the chances of timely treatment and prevents permanent vision impairment. Retinal fundus images are a useful resource to diagnose retinal complications for ophthalmologists. However, manual detection can be laborious and time-consuming. Therefore, developing an automated diagnose system reduces the time and workload for ophthalmologists. Recently, the image classification using Deep Learning (DL) in between healthy or diseased retinal fundus image classification already achieved a state of the art performance. While the classification of mild and multiclass diseases remains an open challenge, therefore, this research aimed to build an automated classification system considering two scenarios: (i) mild multi-class diabetic eye disease (DED), and (ii) multi-class DED. Our model tested on various datasets, annotated by an opthalmologist. The experiment conducted employing the top two pretrained convolutional neural network (CNN) models on ImageNet. Furthermore, various performance improvement techniques were employed, i.e., fine-tune, optimization, and contrast enhancement. Maximum accuracy of 88.3% obtained on the VGG16 model for multi-class classification and 85.95% for mild multi-class classification. Keywords: Diabetic eye disease, Deep learning, Classification, Image processing Introduction The World Health Organisation (WHO) reports, 2.2 billion individuals globally have a blindness or vision loss, of which at least 1 billion have impaired vision, which could have been reversed.1 One of the reasons for this blindness is identified as diabetes mellitus or diabetes. Approximately one-third of those with diabetes expected to diagnosed with a DED, a chronic eye disease that can cause permanent visual impairment if left unattended [10]. DED includes diabetic retinopathy (DR), glaucoma (Gl), diabetic macular edema (DME), and cataract2 (Ca) (see Fig. 1). It is crucial to identify and diagnose these diseases for the treatment. Motivated by the necessity of active strategies for diagnosis, and prevention to implement the broad spectrum of needs associated with retinal disorders and visual impairments throughout the lifespan. Automated DED diagnostic techniques using DL are vital to addressing these issues [1, 18]. Timely screening of DED, which is *Correspondence: [email protected] Victoria University, Ballarat Road, Melbourne, VIC 3011, USA
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crucial to effective prognosis based on professional ophthalmologist, is time and labor intensive [16]. Although DL has generally achieved high validation accuracies for healthy and diseased (binary) classification, the results of mild and multi-class classi
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