"Ar-HGSO: Deep Learning for Diabetic Retinopathy Detection and Severity Classification"

What is it about?

Diabetic Retinopathy (DR) is one the most important problems of diabetics and it directs to the main cause of blindness. When proper treatment is afforded for DR patients, almost 90% of patients are protected from visual damage. DR does not produce any symptoms at the initial phase of the disease, thus various physical assessments, namely pupil dilation, visual acuity test, and so on are needed for DR disease detection. It is more complex to detect the DR during manual testing, because of the variations and complications of DR. The early detection and appropriate treatment assist to prevent vision loss for DR patients. Thus, it is very indispensable to categorize the levels and severity of DR for recommendation of essential treatment. In this paper, Autoregressive-Henry Gas Sailfish Optimization (Ar-HGSO)-based deep learning technique is proposed for DR detection and severity level classification of DR and Macular Edema (ME) based on color fundus images. The segmentation process is more essential for proper detection and classification process, which segments the image into various subgroups. The Deep Learning approach is utilized for effective identification of DR and severity classification of DR and ME. Moreover, the deep learning technique is trained by the designed Ar-HGSO scheme for obtaining better performance. The performance of the devised technique is evaluated using the IDRID dataset and DDR dataset. The introduced Ar-HGSO-based deep learning approach obtained better performance than other existing DR detection and classification techniques with regards to testing accuracy, sensitivity, and specificity of 0.9142, 0.9254, and 0.9142 using the IDRID dataset.

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Why is it important?

Usually, DR disease produced retinal damage, and it directs to vision loss. Hence, early identification of DR severity plays a vital role during treatment. In general, DR affects 80% of peoples, who has diabetes disease It does not create any symptoms in its early phase as well as various peoples may predict minute variations in their sight. The most important source of DR is the worst manage over their blood sugar, and also basic estimation of DR is complex because it is a more time-consuming process. The DR commonly consists of two kinds, which include proliferative and non-proliferative DR. The proliferative DR is the progressive period of the disease, which states to the additional blood vessels retina generation, whereas non-proliferative DR is a milder stage of DR. Moreover, non-proliferative DR comprises various complications, namely hemorrhages, cotton wool spots, hard exudates, and microaneurysms. In addition, proliferative DR is considered by irregular blood vessels in the retina named neovascularization

Perspectives

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This article presents an effective DR detection and severity classification of DR and ME using devised Ar-HGSO-based Deep learning model. The input image is taken from database and is passed to pre-processing process The noises and outliers present in an input image are eliminated using a median filter, which enhanced the image quality and the ROI portions are extracted for further processing.

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