Optimal Feature Selection for Diagnosing Diabetic Retinopathy Using FireFly Migration Operator-Based Monarch Butterfly Optimization

In recent years, diabetic retinopathy (DR) needs to be focused with the intention of developing accurate and effective approaches by accomplishing the existing challenges in the traditional models. With this objective, this paper aims to introduce an effective diagnosis system by utilizing retinal fundus images. The implementation of this diagnosis model incorporates 4 stages like (i) preprocessing, (ii) blood vessel segmentation, (iii) feature extraction, as well as (iv) classification. Originally, the median filter as well as contrast limited adaptive histogram equalization (CLAHE) help to preprocess the image. Moreover, the Fuzzy C Mean (FCM) thresholding is applied for blood vessel segmentation, which generates stochastic clustering of pixels to obtain enhanced threshold values. Further, feature extraction is accomplished by utilizing gray-level run-length matrix (GLRM), local, and morphological transformation-based features. Furthermore, a deep learning (DL) model known as convolutional neural network (CNN) is employed for the diagnosis or classification purpose. As a main novelty, this paper introduces an optimal feature selection as well as classification model. Further, the feature selection is done optimally by FireFly Migration Operator-based Monarch Butterfly Optimization (FM-MBO) which hybridized of the monarch butterfly optimization (MBO) and fire fly (FF) algorithms as the entire adopted extracted features attain higher feature length. Moreover, the proposed FM-MBO algorithm helps for optimizing the count of CNN's convolutional neurons to further improve the performance accuracy. At the end, the enhanced outcomes of the adopted diagnostic scheme are validated via a valuable comparative examination in terms of significant performance measures.