A Comprehensive Study of Supervised Learning Models on the OCTID Dataset for Retinal Disease Detection with Future Directions
Abstract
This paper presents a critical analysis of recent approaches to supervised learning techniques on the OCTID database that are important in the early diagnosis of retinal diseases such as AMD, CSR, DME, and MH. The review concentrates on methodologies, experimental configurations, and assessments of various machine learning and deep learning techniques. The limitations observed in the current work are in terms of computational complexity, possible limitation in labeling, and lack of extensive experimentation using videos with OCT. For future work, we suggest using semi-supervised learning as they are more accurate and less costly in medical applications.
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Aggarwal, Pushkar. "Machine learning of retinal pathology in optical coherence tomography images." Journal of Medical Artificial Intelligence 2 (2019).
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.
Gholami, Peyman, et al. "OCTID: Optical coherence tomography image database." Computers & Electrical Engineering 81 (2020): 106532.
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.
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.
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.
Mendes, Odilon LC, et al. "Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images." IEEE Access 9 (2021): 96487-96500.
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.
Shaker, Fariba, et al. "Application of Deep Dictionary Learning and Predefined Filters for Classification of Retinal Optical Coherence Tomography Images.".
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.
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.
DOI: http://dx.doi.org/10.18415/ijmmu.v11i11.6403
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