Adaptive machine learning classification for diabetic retinopathy
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Adaptive machine learning classification for diabetic retinopathy Laxmi Math 1 & Ruksar Fatima 1 Received: 3 January 2020 / Revised: 27 August 2020 / Accepted: 2 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Diabetic retinopathy is the main cause of the blindness worldwide. However, the diabetic retinopathy is hard to be detected in the initial stages, and the procedure of diagnostic can be time-consuming even for experienced-experts. The segment based learning approach has shown the benefits over learning technique for detection of diabetic retinopathy: only the annotation of image level is required get the lesions and detection of diabetic retinopathy. Anyways, the performance of existing methods are limited by the utilization of handcrafted features. This paper proposes the segment based learning approach for detection of diabetic retinopathy, which mutually learns classifiers and features from the data and gets significant development on recognizing the images of diabetic retinopathy and their inside the lesions. Specifically, the pre-trained CNN is adapted to get the segment level DRE (Diabetic retinopathy Estimation) and then Integrating all segment level of (DRM) is utilized to make the classification of diabetic retinopathy images. Lastly, an end-to-end segment based learning approach to deal with the irregular lesions of diabetic retinopathy. For detection of the diabetic retinopathy images obtain area under of ROC curve is 0.963 on the Kaggle dataset and also obtains sensitivity and specificity 96.37% and 96.37% on the higher specificity and sensitivity that outperforms much better than existing model. Keywords Diabetic retinopathy . CNN (convolution neural networks) . DL (deep learning) . Fundus images
* Laxmi Math [email protected] Ruksar Fatima [email protected]
1
KBN College of Engineering, Kalaburagi, Karnataka, India
Multimedia Tools and Applications
1 Introduction Diabetic Retinopathy is one of the main cause of preventable vision loss among the working aged adults in world [20]. It is expected 35% of patients with DM (diabetic mellitus) suffer from the diabetic retinopathy. The risk of diabetic retinopathy maximizes as longer a person has the DM. According to WHO, diabetic retinopathy is expected to harm more than 77% of patients who had diabetes 20 yrs. and more [15]. Fundus photography is commonly utilized the imaging method for identification of diabetic retinopathy in retina. It has been utilized for the diabetic retinopathy screening due to its low cost, transmission, high-resolution and easy storage. To simplify the identification of diabetic retinopathy, many classification methods have been improved to categorize the severity of diabetic retinopathy in mass screening in that the severity levels of diabetic retinopathy are well defined by various diabetic retinopathy related to the features. The diabetic retinopathy treatment is not easy as there is no symptom, which is shown at initial phases of the diabetic retinopathy, and rarely
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