ArticleDiagnostics (Basel, Switzerland)2023
Classification of Retinal Diseases in Optical Coherence Tomography Images Using Artificial Intelligence and Firefly Algorithm.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Language assisted learnable hyperdimensional computing framework for retinal disease classification.Scientific reports · 2026Article
- Neurosymbolic AI Framework for Explainable Retinal Disease Classification From OCT Images.Translational vision science & technology · 2026Article
- An artificial intelligence cloud platform for OCT-based retinal anomalies screening system in real clinical environments.NPJ digital medicine · 2025Article
- Pan-Ret: a semi-supervised framework for scalable detection of pan-retinal diseases.Medical & biological engineering & computing · 2025Review
- A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis.Journal of healthcare informatics research · 2025Article
- Evaluating deep learning models for classifying OCT images with limited data and noisy labels.Scientific reports · 2024Article
- A new intelligent system based deep learning to detect DME and AMD in OCT images.International ophthalmology · 2024Article
- Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture.Bioengineering (Basel, Switzerland) · 2023Article
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3 authors.
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Abstract
In recent years, the number of studies for the automatic diagnosis of biomedical diseases has increased. Many of these studies have used Deep Learning, which gives extremely good results but requires a vast amount of data and computing load. If the processor is of insufficient quality, this takes time and places an excessive load on the processor. On the other hand, Machine Learning is faster than Deep Learning and does not have a much-needed computing load, but it does not provide as high an accuracy value as Deep Learning. Therefore, our goal is to develop a hybrid system that provides a high accuracy value, while requiring a smaller computing load and less time to diagnose biomedical diseases such as the retinal diseases we chose for this study. For this purpose, first, retinal layer extraction was conducted through image preprocessing. Then, traditional feature extractors were combined with pre-trained Deep Learning feature extractors. To select the best features, we used the Firefly algorithm. In the end, multiple binary classifications were conducted instead of multiclass classification with Machine Learning classifiers. Two public datasets were used in this study. The first dataset had a mean accuracy of 0.957, and the second dataset had a mean accuracy of 0.954.
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Registered trials
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