Evaluation of Machine Learning and Deep Learning Approaches for Automatic Detection of Eye Diseases

DOI: https://doi.org/10.58190/imiens.2024.85

Keywords

Classification, Deep features, Deep learning, Eye diseases, Machine learning

Abstract

There are many ocular diseases present in the world. These diseases may arise from factors such as genetic predisposition, environmental influences, and aging. In recent years, advancements in technology have facilitated the detection of ocular pathologies through machine learning techniques. Machine learning models can serve as decision support mechanisms in diagnostic scenarios. In this study, the aim is to detect ocular diseases using machine learning and deep learning techniques. To enhance the results obtained from classification with 4,217 images in the study, 705 images were added to the glaucoma class and 370 images were added to the Diabetic Retinopathy class. The supplemented dataset with additional images comprises a total of four classes. One class represents the control group and is labeled as "normal." The remaining three classes represent disease categories: Diabetic Retinopathy, Cataract, and Glaucoma. To extract deep features from the images, a pre-trained InceptionV3 model was utilized, resulting in 2048 features extracted. These extracted features were then classified using Neural Network (NN), Logistic Regression (LR), k-Nearest Neighbors (k-NN), and Random Forest (RF) machine learning models. Before the dataset supplemented with additional images, the classification accuracies of the machine learning models were as NN 89.2%, LR 87.3%, k-NN 81.2%, and Random Forest 76.9%. Upon examining the classification accuracies after dataset supplemented with additional images, the following improvements were observed: NN 90.9% with a 1.7% increase, LR 90.2% with a 2.9% increase, k-NN 84.6% with a 3.4% increase, and Random Forest 82% with a 5.1% increase. Performance evaluation was conducted using recall, precision, and F-1 score metrics. Additionally, the learning performance of the machine learning models was assessed through Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) values.

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    • Published: 2023-03-29

    Issue: Vol. 3 No. 1 (2024) (view)

    Section: Research Articles

    How to cite:
    [1]
    İbrahim KAYA and İlkay ÇINAR, “Evaluation of Machine Learning and Deep Learning Approaches for Automatic Detection of Eye Diseases”, Intell Methods Eng Sci, vol. 3, no. 1, pp. 37–45, Mar. 2023.

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