Diabetic Retinopathy Detection Using Genetic Algorithm-Based CNN Features and Error Correction Output Code SVM Framework Classification Model

Abstract

Diabetic retinopathy (DR) is a type of eye disease that may be caused in individuals suffering from diabetes which results in vision loss. DR identification and routine diagnosis is a challenging task and may need several screenings. Early identification of DR has the potential to prevent or delay vision loss. For real-time applications, an automated DR identification approach is required to assist and reduce possible human mistakes. In this research work, we propose a deep neural network and genetic algorithm-based feature selection approach. Five advanced convolutional neural network architectures are used to extract features from the fundus images, i.e., AlexNet, NASNet-Large, VGG-19, Inception V3, and ShuffleNet, followed by genetic algorithm for feature selection and ranking features into high rank (optimal) and lower rank (unsatisfactory). The nonoptimal feature attributes from the training and validation feature vectors are then dropped. Support vector machine- (SVM-) based classification model is used to develop diabetic retinopathy recognition model. The model performance is evaluated using accuracy, precision, recall, and F1 score. The proposed model is tested on three different datasets: the Kaggle dataset, a self-generated custom dataset, and an enhanced custom dataset with 97.9%, 94.76%, and 96.4% accuracy, respectively. In the enhanced custom dataset, data augmentation has been performed due to the smaller size of the dataset and to eliminate the noise in fundus images.

Detection and Grade Classification of Diabetic Retinopathy and Adult Vitelliform Macular Dystrophy Based on Ophthalmoscopy Images

“…This disease normally does not impact a person's ability to see in the periphery (also known as peripheral vision) or their capacity to see in the dark. Figure 1 depicts the diabetic retinopathy and adult vitelliform macular images [9]. One of the primary contributors to visual loss in the senior population is age-related macular degeneration (AMD).…”

Enhanced Diabetic Retinopathy Diagnosis: A Comparative Analysis of models with an Ensemble classifier and Deep Q Learning

“…A feature selection method based on genetic algorithms and deep neural networks was presented in this research study [15]. A genetic approach was utilized for a selection of features and to classify the features into high and low ranks after five sophisticated convolutional neural network architectures, AlexNet, NASNet-Large, VGG-19, Inception V3, and ShuffleNet were utilized to retrieve features from the retinal fundus images.…”

Biometric System for Person Authentication Using Retinal Vascular Branching Pattern

“…This is done by researchers to see the results of the accuracy of retinal image identification and the length of computation time in identifying retinal images. SVM formation uses the parameter Error Correcting Output Codes (ECOC) [28] with onevs-one (OVO) encoding. The ECOC parameter forms a binary label for each class to be formed.…”