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An Empirical Study of Preprocessing Techniques with Convolutional Neural Networks for Accurate Detection of Chronic Ocular Diseases Using Fundus Images by Veena Mayya; Sowmya Kamath S; Uma Kulkarni; Divyalakshmi Kaiyoor Surya; U Rajendra Acharya is a Computer Science article available to read on EtoBox.
What is An Empirical Study of Preprocessing Techniques with Convolutional Neural Networks for Accurate Detection of Chronic Ocular Diseases Using Fundus Images about?
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 k
Who reads An Empirical Study of Preprocessing Techniques with Convolutional Neural Networks for Accurate Detection of Chronic Ocular Diseases Using Fundus Images?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Veena Mayya; Sowmya Kamath S; Uma Kulkarni; Divyalakshmi Kaiyoor Surya; U Rajendra Acharya
- Publisher
- Springer Science and Business Media LLC
- Published
- 2022
- Language
- EN
- Field
- Computer Science (Physical Sciences)