This study presents a comprehensive evaluation of various supervised machine learning models for the automated detection and classification of retinal diseases using the Optical Coherence Tomography Image Database (OCTID). Retinal diseases, such as Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), and Macular Hole (MH), are leading causes of irreversible vision loss, and early, accurate diagnosis is crucial for effective treatment and prognosis. Optical Coherence Tomography (OCT) has revolutionized ophthalmic diagnostics by providing high-resolution cross-sectional images of the retina. The advent of large, publicly available datasets like OCTID offers unprecedented opportunities for developing and benchmarking automated diagnostic systems. This research systematically investigates the performance of both traditional machine learning classifiers (e.g., Support Vector Machines, Random Forests) with handcrafted features and advanced deep learning architectures (e.g., Convolutional Neural Networks) on the OCTID dataset. Through rigorous experimental protocols, including standardized preprocessing and evaluation metrics, the study compares the diagnostic accuracy, precision, recall, and F1-score of these models across different retinal pathologies. Findings indicate that deep learning models generally outperform traditional approaches, demonstrating superior capability in extracting complex, discriminative features directly from raw OCT images. This comprehensive analysis provides valuable insights into the current state-of-the-art in automated retinal disease detection using supervised learning and identifies critical future directions for enhancing diagnostic precision and clinical utility.

Retinal Disease Detection, Optical Coherence Tomography (OCT), OCTID Dataset

1. Aggarwal, Pushkar. "Machine learning of retinal pathology in optical coherence tomography images." Journal of Medical Artificial Intelligence 2 (2019).

2. Baharlouei, Zahra, Hossein Rabbani, and Gerlind Plonka. "Wavelet scattering transform application in classification of retinal abnormalities using OCT images." Scientific Reports 13.1 (2023): 19013.

3. Gholami, Peyman, et al. "OCTID: Optical coherence tomography image database." Computers & Electrical Engineering 81 (2020): 106532.

4. Hassan, Syed Al E., et al. "Deep learning-based automatic detection of central serous retinopathy using optical coherence tomographic images." 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA). IEEE, 2021.

5. Hammou, Badr Ait, et al. "MBT: Model-Based Transformer for retinal optical coherence tomography image and video multi-classification." International Journal of Medical Informatics 178 (2023): 105178.

6. Koseoglu, Neslihan Dilruba, Andrzej Grzybowski, and TY Alvin Liu. "Deep learning applications to classification and detection of age-related macular degeneration on optical coherence tomography imaging: a review." Ophthalmology and Therapy 12.5 (2023): 2347-2359.

7. Mendes, Odilon LC, et al. "Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images." IEEE Access 9 (2021): 96487-96500.

8. Marciniak, Tomasz, and Agnieszka Stankiewicz. "Automatic diagnosis of selected retinal diseases based on OCT B-scan." Klinika Oczna/Acta Ophthalmologica Polonica 126.1 (2023): 8-14.

9. Shaker, Fariba, et al. "Application of Deep Dictionary Learning and Predefined Filters for Classification of Retinal Optical Coherence Tomography Images."

10. Sahoo, Moumita, Madhuchhanda Mitra, and Saurabh Pal. "Improved detection of dry age-related macular degeneration from optical coherence tomography images using adaptive window based feature extraction and weighted ensemble based classification approach." Photodiagnosis and Photodynamic Therapy 42 (2023): 103629.

11. Thomas, Anju, et al. "Automated Detection of Age-Related Macular Degeneration from OCT. Images Using Multipath CNN." J. Comput. Sci. Eng. 15.1 (2021): 34-46.

12. tic Macular Edema (DME), Macular Hole (MH)