Int J Numer Method Biomed Eng. 2021 Dec 4:e3560. doi: 10.1002/cnm.3560. Online ahead of print.
ABSTRACT
Because of retina abnormalities of diabetic patients, the most common vision-threatening disease is Diabetic retinopathy (DR). The diabetic retinopathy diagnosis and prevention is the challenging task it may lead to vision loss. According to the literature analysis, the shortcomings in existing studies such as failed to reduce the feature dimension, higher execution time and higher computational cost, unable to tune the hyper-parameters such as a number of hidden layers and learning rate, more computational complexities, higher cost and etc during DR classification. To tackle these problems, we proposed a Deep long and short term memory (LSTM) in a neural network with Red Fox Optimization (Deep LSTM-RFO) algorithm for DR classification. The four major components involved in the proposed methods are image pre-processing, segmentation, feature extraction and classification. At first, an adaptive histogram equalization (CLAHE) and histogram equalization (HE) model performs the fundus image pre-processing thereby neglecting the noise and improving the contrast level of an image. Next, an adaptive watershed segmentation model effectively segments the lesion region based on the optic disc color and size of hemorrhages. At the third stage, we have extracted statistical, intensity, color and shape features. Finally, the single normal class with three abnormal classes such as mild Non-proliferative diabetic retinopathy (M-NPDR), moderate NPDR (Mo-NPDR) and severe NPDR (S-NPDR) are accurately classified using the Deep LSTM-RFO algorithm. Experimentally, the MESSIDOR, STARE and DRIVE datasets are the datasets used for both training and validation. MATLAB software performs the implementation process with respect to various evaluation criteria used. However, the proposed method accomplished superior performance such as 98.45% specificity, 96.78% sensitivity, 97.92% precision, 96.89% recall, 97.93% F-score results in terms of DR classification than previous methods. This article is protected by copyright. All rights reserved.
PMID:34865312 | DOI:10.1002/cnm.3560
