Deep neural networks to predict diabetic retinopathy

• Published in: Journal of ambient intelligence and humanized computing Vol. 14; no. 5; pp. 5407 - 5420
• Main Authors: Gadekallu, Thippa Reddy, Khare, Neelu, Bhattacharya, Sweta, Singh, Saurabh, Maddikunta, Praveen Kumar Reddy, Srivastava, Gautam
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Artificial neural networks; Automation; Blood vessels; Computational Intelligence; Datasets; Decision trees; Deep learning; Diabetes; Diabetic retinopathy; Engineering; Glucose; Insulin; Machine learning; Mathematical models; Neural networks; Optimization techniques; Original Research; Parameters; Patients; Principal components analysis; Retina; Robotics and Automation; Support vector machines; User Interfaces and Human Computer Interaction; Dimensionality reduction; Deep learning; Diabetic retinopathy; Deep neural networks; Principal component analysis; Grey Wolf Optimizer (GWO)
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-020-01963-7

Diabetic retinopathy is a prominent cause of blindness among elderly people and has become a global medical problem over the last few decades. There are several scientific and medical approaches to screen and detect this disease, but most of the detection is done using retinal fungal imaging. The present study uses principal component analysis based deep neural network model using Grey Wolf Optimization (GWO) algorithm to classify the extracted features of diabetic retinopathy dataset. The use of GWO enables to choose optimal parameters for training the DNN model. The steps involved in this paper include standardization of the diabetic retinopathy dataset using a standardscaler normalization method, followed by dimensionality reduction using PCA, then choosing of optimal hyper parameters by GWO and finally training of the dataset using a DNN model. The proposed model is evaluated based on the performance measures namely accuracy, recall, sensitivity and specificity. The model is further compared with the traditional machine learning algorithms—support vector machine (SVM), Naive Bayes Classifier, Decision Tree and XGBoost. The results show that the proposed model offers better performance compared to the aforementioned algorithms.