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Open Access Journal Article

Hybrid Image Features Supported Normal/Abnormal Retinal Optical Coherence Tomography Image Classification

by Aleksandrs Mārtiņš 1,* orcid
1
Pauls Stradiņš Clinical University Hospital, Pilsoņu iela 13, Zemgales priekšpilsēta, Rīga, LV-1002, Latvia
*
Author to whom correspondence should be addressed.
IJCMR  2024 3(1):46; https://doi.org/10.61466/ijcmr3010001
Received: 26 November 2024 / Accepted: 27 December 2024 / Published Online: 31 December 2024
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Abstract

Background

Eye is one of the primary sensory organs, responsible to provide the necessary visual information to the brain. Any abnormality in eye cause mild to severe vision issues and hence appropriate detection and treatment is preferred. Clinical level examination of the eye is commonly performed using image examination procedures and this work considered the Optical Coherence Tomography (OCT) for the study. In the present study, the proposed system aims to develop a tool to classify the OCT-images into normal and abnormal class using the traditional deep-learning (DL) and machine-learning (ML) features.

Methods

This tool consists the following stages; OCT-image collection and resizing based on the chosen DL scheme, image features extraction using DL and ML methods, DL feature reduction using 50% dropout and serially fusing the deep- and machine-features to generate hybrid-features, and binary classification using 5-fold cross-validation.

Results

The developed tool’s merit is verified using; (i) deep-features, (ii) machine-features, and (iii) hybrid-features. The merit of the developed scheme is verified using different binary classifiers and the overall quality metric is considered to verify the tool’s performance.

Conclusions

The study confirms that the hybrid-features based detection provides 97.6% accuracy, when Random Forest classifier is employed, which verifies the tool’s merit on the chosen OCT database. In the future, this tool can be considered to detect the common AEAs, like AMD and the DME.

Keywords: retinal abnormality, Optical Coherence Tomography, deep learning, hybrid features;
Copyright: © 2024 by Mārtiņš. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Mārtiņš, A. Hybrid Image Features Supported Normal/Abnormal Retinal Optical Coherence Tomography Image Classification. International Journal of Clinical Medical Research, 2025, 3, 46. https://doi.org/10.61466/ijcmr3010001
AMA Style
Mārtiņš A. Hybrid Image Features Supported Normal/Abnormal Retinal Optical Coherence Tomography Image Classification. International Journal of Clinical Medical Research; 2025, 3(1):46. https://doi.org/10.61466/ijcmr3010001
Chicago/Turabian Style
Mārtiņš, Aleksandrs 2025. "Hybrid Image Features Supported Normal/Abnormal Retinal Optical Coherence Tomography Image Classification" International Journal of Clinical Medical Research 3, no.1:46. https://doi.org/10.61466/ijcmr3010001
APA style
Mārtiņš, A. (2025). Hybrid Image Features Supported Normal/Abnormal Retinal Optical Coherence Tomography Image Classification. International Journal of Clinical Medical Research, 3(1), 46. https://doi.org/10.61466/ijcmr3010001

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