Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural network

Abstract

Background: Glaucoma and diabetic retinopathy are the leading causes of blindness due to an irreversible damage to the retina which results in vision loss. Early detection of these diseases through regular screening is especially important to prevent progression. The image of retinal fundus is the main evaluating strategy for the glaucoma and diabetic retinopathy detection. Then, automated eye disease detection is an important application of retinal image analysis. Compared with classical diagnostic techniques, image classification by convolutional neural networks (CNN) have the potential for better cost-effective performance. Methods: In this paper, we propose the use of MATLAB – retrained AlexNet CNN for computerized eye diseases identification, particularly glaucoma and diabetic retinopathy, by employing retinal fundus images. The acquisition of the database was carried out through free access databases and access upon request. A transfer learning technique is used for retraining the AlexNet CNN. Specifically, the model divides the fundus image dataset into training and testing data. Results: As datasets were added by network training, different values were reported for validation accuracy, false positives and false negatives, precision, and recall. Thus, having NetTransfer I with a validation accuracy value of 94.3% for two classes. NetTransfer II with a validation accuracy value of 91.8% for two classes. NetTransfer III with a validation accuracy value of 89.7% for three classes. Net transfer IV with a validation accuracy value of 93.1% for three classes. Finally, NetTransfer V with a validation accuracy value of 92.1% for three classes. Conclusions: Re-training of the AlexNet network proved to be a powerful tool to create disease detection systems having high accuracy values and being able to discern between more than two diseases.

Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence

“…The study [14] used a radial basis function neural network to detect DR with an accuracy of 89.4%. Using a retrained AlexNet CNN, the study [15] obtained 92.5% accuracy for DR diagnosis in another investigation.…”

“…Proposed Method Accuracy [15] AlexNet CNN 92.50% [13] KOA Based on BILSTM Network 84.09% [51] Deep learning-based method 90.60% [52] Hybrid deep learning 93.84% [53] Linear Model and Naïve Bayes 75.13% [54] Ensemble learning method 89.50% [55] SVM and DL 84.60% [56] Neural network 90.00% [22] RetNet-10 method 98.65% [23] DR-CCTNet method 90.17% Proposed Model CNN(Proposed) 95.27%…”

Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images

“…This acceleration is achieved by leveraging pre-implemented functions for preprocessing, training, validation, and testing. MATLAB holds considerable prevalence in medical research and is a robust tool, as demonstrated by its application in recent drug-related studies leading to FDA approval [ 19 ].…”

Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

“…Consecutively [33], different databases of more than two diseases were used and gave acceptable validation ACC for the training process. This process was checked by five different versions of the AlexNet CNN (Net transfer I, Net transfer II, Net transfer III, Net transfer IV, and Net transfer V): 94.30%, 91.8%, 89.7%, 93.1%, and 92.10% successively [34]. Sharif A. Kamran presented the generative adversarial network (VTGAN).…”