Research Article

Performance Analysis of Modern and Traditional Convolutional Neural Networks in the Classification of Optical Coherence Tomography (OCT) Images

Volume: 6 Number: 4 July 31, 2026

Performance Analysis of Modern and Traditional Convolutional Neural Networks in the Classification of Optical Coherence Tomography (OCT) Images

Abstract

Optical Coherence Tomography (OCT) plays a critical role in the early diagnosis of retinal diseases. In this study, a deep learning-based automatic diagnosis system was developed using the OCTMNIST dataset, which includes Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and normal retina images. This study was conducted to measure the capability of artificial intelligence in this field. To increase rigorous evaluation and prevent data leakage, the dataset was separated into isolated folder structures: 80% training, 10% validation, and 10% testing. Within the scope of the study, five different models consisting of traditional and modern Convolutional Neural Network (CNN) architectures (ResNet18, MobileNetV2, EfficientNetB0, DenseNet121, and VGG16) were trained and their performances were compared. During the training process, Automatic Mixed Precision (AMP) was used for hardware optimization, and Early Stopping algorithms were utilized to prevent overfitting. According to the test results, the EfficientNetB0 model achieved the highest success with 93.67% accuracy and a 0.88 F1-Score. In contrast, older traditional architectures like VGG16 required architecture-specific hyperparameter tuning just to converge, highlighting the superior stability of modern CNNs. The research results demonstrate that modern lightweight CNN architectures offer high efficiency in medical image processing. Furthermore, to provide a practical approach for clinical use, the most successful model was integrated into a web-based interface and presented to the end-user. This study reveals the performance differences of deep learning models under hardware constraints and proposes an end-to-end artificial intelligence solution that will accelerate medical diagnosis processes.

Keywords

References

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Details

Primary Language

English

Subjects

Electrical Engineering (Other)

Journal Section

Research Article

Publication Date

July 31, 2026

Submission Date

April 17, 2026

Acceptance Date

July 28, 2026

Published in Issue

Year 2026 Volume: 6 Number: 4

APA
Kayaalp, K., & Onay, İ. (2026). Performance Analysis of Modern and Traditional Convolutional Neural Networks in the Classification of Optical Coherence Tomography (OCT) Images. Engineering Perspective, 6(4), 553-560. https://doi.org/10.64808/engineeringperspective.1932616
AMA
1.Kayaalp K, Onay İ. Performance Analysis of Modern and Traditional Convolutional Neural Networks in the Classification of Optical Coherence Tomography (OCT) Images. engineeringperspective. 2026;6(4):553-560. doi:10.64808/engineeringperspective.1932616
Chicago
Kayaalp, Kıyas, and İlkay Onay. 2026. “Performance Analysis of Modern and Traditional Convolutional Neural Networks in the Classification of Optical Coherence Tomography (OCT) Images”. Engineering Perspective 6 (4): 553-60. https://doi.org/10.64808/engineeringperspective.1932616.
EndNote
Kayaalp K, Onay İ (July 1, 2026) Performance Analysis of Modern and Traditional Convolutional Neural Networks in the Classification of Optical Coherence Tomography (OCT) Images. Engineering Perspective 6 4 553–560.
IEEE
[1]K. Kayaalp and İ. Onay, “Performance Analysis of Modern and Traditional Convolutional Neural Networks in the Classification of Optical Coherence Tomography (OCT) Images”, engineeringperspective, vol. 6, no. 4, pp. 553–560, July 2026, doi: 10.64808/engineeringperspective.1932616.
ISNAD
Kayaalp, Kıyas - Onay, İlkay. “Performance Analysis of Modern and Traditional Convolutional Neural Networks in the Classification of Optical Coherence Tomography (OCT) Images”. Engineering Perspective 6/4 (July 1, 2026): 553-560. https://doi.org/10.64808/engineeringperspective.1932616.
JAMA
1.Kayaalp K, Onay İ. Performance Analysis of Modern and Traditional Convolutional Neural Networks in the Classification of Optical Coherence Tomography (OCT) Images. engineeringperspective. 2026;6:553–560.
MLA
Kayaalp, Kıyas, and İlkay Onay. “Performance Analysis of Modern and Traditional Convolutional Neural Networks in the Classification of Optical Coherence Tomography (OCT) Images”. Engineering Perspective, vol. 6, no. 4, July 2026, pp. 553-60, doi:10.64808/engineeringperspective.1932616.
Vancouver
1.Kıyas Kayaalp, İlkay Onay. Performance Analysis of Modern and Traditional Convolutional Neural Networks in the Classification of Optical Coherence Tomography (OCT) Images. engineeringperspective. 2026 Jul. 1;6(4):553-60. doi:10.64808/engineeringperspective.1932616

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