An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images
Article
Mayya, Veena, Kamath S, Sowmya, Kulkarni, Uma, Surya, Divyalakshmi Kaiyoor and Acharya, U Rajendra. 2023. "An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images." Applied Intelligence. 53 (2), pp. 1548-1566. https://doi.org/10.1007/s10489-022-03490-8
| Article Title | An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images |
|---|---|
| ERA Journal ID | 17757 |
| Article Category | Article |
| Authors | Mayya, Veena, Kamath S, Sowmya, Kulkarni, Uma, Surya, Divyalakshmi Kaiyoor and Acharya, U Rajendra |
| Journal Title | Applied Intelligence |
| Journal Citation | 53 (2), pp. 1548-1566 |
| Number of Pages | 19 |
| Year | 2023 |
| Publisher | Springer |
| Place of Publication | United States |
| ISSN | 0924-669X |
| 1573-7497 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s10489-022-03490-8 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s10489-022-03490-8 |
| Abstract | Chronic Ocular Diseases (COD) such as myopia, diabetic retinopathy, age-related macular degeneration, glaucoma, and cataract can affect the eye and may even lead to severe vision impairment or blindness. According to a recent World Health Organization (WHO) report on vision, at least 2.2 billion individuals worldwide suffer from vision impairment. Often, overt signs indicative of COD do not manifest until the disease has progressed to an advanced stage. However, if COD is detected early, vision impairment can be avoided by early intervention and cost-effective treatment. Ophthalmologists are trained to detect COD by examining certain minute changes in the retina, such as microaneurysms, macular edema, hemorrhages, and alterations in the blood vessels. The range of eye conditions is diverse, and each of these conditions requires a unique patient-specific treatment. Convolutional neural networks (CNNs) have demonstrated significant potential in multi-disciplinary fields, including the detection of a variety of eye diseases. In this study, we combined several preprocessing approaches with convolutional neural networks to accurately detect COD in eye fundus images. To the best of our knowledge, this is the first work that provides a qualitative analysis of preprocessing approaches for COD classification using CNN models. Experimental results demonstrate that CNNs trained on the region of interest segmented images outperform the models trained on the original input images by a substantial margin. Additionally, an ensemble of three preprocessing techniques outperformed other state-of-the-art approaches by 30% and 3%, in terms of Kappa and F1 scores, respectively. The developed prototype has been extensively tested and can be evaluated on more comprehensive COD datasets for deployment in the clinical setup. |
| Keywords | Convolutional neural networks; Healthcare informatics ; Clinical decision support systems ; Explainability ; Fundus imaging |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | National Institute of Technology Karnataka, India |
| Manipal Academy of Higher Education, India | |
| Yenepoya University, India | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
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