Abstract
Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.
Highlights
Diabetes is a disorder in which the glucose level of patients is higher than the normal level
Diabetes victims are at risk of various eye diseases, such as diabetic retinopathy (DR), diabetic macular edema (DME), cataract, and glaucoma
This can lead to proliferative DR (PDR), in which new blood vessels are formed on the retina and later on the surface of the vitreous
Summary
Diabetes is a disorder in which the glucose level of patients is higher than the normal level. DR is an eye disease that affects blood vessels of the retina of diabetic patients, and its signs include microaneurysms, hemorrhages, and soft and hard exudates. DR starts with minor abnormalities, in which vascular permeability increases This can lead to PDR, in which new blood vessels are formed on the retina and later on the surface of the vitreous. DR is often undetected until it progresses to an advanced stage and leads to DME, which results in severe loss of vision. Patients who have mild DR do not require any specific medical treatment, they should ensure their diabetes is well controlled They should be monitored periodically, to prevent the development of advanced stages of DR. The presented Custom CenterNet can detect all types of lesions, including early and severe signs of eye diseases. The rest of the paper is organized as follows: Section 2 describes the proposed method; Section 3 consists of the experimental results and comparative analysis; Section 5 concludes our proposed work
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