Manuscript received July 31, 2024; revised August 7, 2024; accepted September 25, 2024; published January 21, 2025
Abstract—Nowadays, there are still significant challenges
encountered in the accurate diagnosis of various eye diseases,
such as Keratoconus (KCN) and cataracts. Early detection of
Keratoconus is crucial in preventing its progression and
ensuring the best treatment outcomes. Artificial Intelligence (AI)
is being widely applied in ophthalmology through the training
of deep learning networks for the early detection of eye diseases.
This research presents a novel, integrated machine learning
approach for diagnosing Keratoconus disease by combining
feature extraction through Convolutional Neural Networks
(CNN) with a Support Vector Machine (SVM) and Artificial
Neural Network (ANN) for classification. Employing a multiobjective
genetic algorithm, the method optimizes feature
selection, aiming to minimize both diagnostic error and the
number of features. The study utilizes a dataset of 5,152
ophthalmic images (1288 samples) categorized into Normal
(476), Suspect (453), and Keratoconus (359) cases. Combining a
Convolutional Neural Network (CNN) for feature selection with
a Genetic Algorithm (GA) significantly improved diagnostic
accuracy. Consequently, by focusing on the most relevant
features of Keratoconus, the model achieved an impressive 98.63%
accuracy for ANN classification with a genetic algorithm, and
98.13% for SVM classification with a genetic algorithm. The
accuracy of the algorithm exceeded that of when SVM and ANN
were used without the genetic algorithm, which were 97.53%
and 96.9% respectively, underscoring the benefit of combining
Artificial Neural Networks (ANNs) with Genetic Algorithms
(GAs) in KC diagnosis. Implementing this model can assist
physicians in more accurate Keratoconus detection, providing
better predictions regarding patients’ eye conditions, and
offering timely treatment recommendations.
Keywords—Keratoconus (KCN) eye disease, Convolutional
Neural Networks (CNN), Support Vector Machine (SVM),
Artificial Neural Network (ANN), Multi-objective Genetic
Algorithm
Cite: Somayeh Ghasedi and Amr R. Abdel-Dayem, "Hybrid Deep Learning and Genetic Algorithm Approach for Detecting Keratoconus Using Corneal Tomography," International Journal of Machine Learning vol. 15, no. 1, pp. 1-12, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).