Abstract
Imaging and computer vision systems offer the ability to study quantitatively on human physiology. On contrary, manual interpretation requires tremendous amount of work, expertise and excessive processing time. This work presents an algorithm that integrates image processing and machine learning to diagnose diabetic retinopathy from retinal fundus images. This automated method classifies diabetic retinopathy (or absence thereof) based on a dataset collected from some publicly available database such as DRIDB0, DRIDB1, MESSIDOR, STARE and HRF. Our approach utilizes bag of words model with Speeded Up Robust Features and demonstrate classification over 180 fundus images containing lesions (hard exudates, soft exudates, microaneurysms, and haemorrhages) and non-lesions with an accuracy of 94.4%, precision of 94%, recall and f1-score of 94% and AUC of 95%. Thus, the proposed approach presents a path toward precise and automated diabetic retinopathy diagnosis on a massive scale.
Highlights
World Health Organization on global report on diabetes, 2016, estimates that the number of people with diabetes has risen from 108 million in 1980 to 422 million in 2014
Our approach utilizes bag of words model with Speeded Up Robust Features and demonstrate classification over 180 fundus images containing lesions and non-lesions with an accuracy of 94.4%, precision of 94%, recall and f1-score of 94% and area under curve (AUC) of 95%
One of the major complications associated with diabetes is diabetic retinopathy (DR) which leads to visual impairment in long term
Summary
World Health Organization on global report on diabetes, 2016, estimates that the number of people with diabetes has risen from 108 million in 1980 to 422 million in 2014. One of the major complications associated with diabetes is diabetic retinopathy (DR) which leads to visual impairment in long term. One out of three diabetic person demonstrates signs of DR [1] and one out of ten suffers from its most severe and vision threatening forms [2]. Diabetic retinopathy results in formation of new retinal blood vessels and leakage from retinal tissues and blood vessels. Diabetic retinopathy is characterized by group of lesions. Often arranged in clumps or rings, appears as yellowish-white deposits with sharp margins. The debris accumulation appearing as fluffy white lesions in the retinal Nerve Fibre Layer is called soft exudates or cotton wool spots. The red spots with irregular margin and sharp edges are called hemorrhages and microaneurysms respectively.
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