Abstract
A survey on diabetic patients in India estimates about 72.96 million cases that are reported under treatment among which 3–7.8% of population are below 20 years. Such kind of diabetes not only affects the body parts but also interpreted with eye diseases based on their physical condition. In general diabetes, patients attacked with eye diseases are categorized as diabetic retinopathy (DR), glaucoma and diabetic macular oedema (DME). These kinds of diseases are common for diabetic patients and if it is not detected early, will lead to loss of vision. The manual diagnosis process is done using ophthalmoscope test which allows checking at the back side of eye and diagnosing process may sometime lead to misdiagnosis, and the fundus images retrieved from ophthalmologist may be cost-effective and time consuming. This paper describes about these three types of diseases and the approach for identification of optimized classification of retinal based eye diseases. Recurrent convolution neural networks are used to categorize the level of eye diseases in diabetic patients. This approach provides an effective and efficient result for diabetes patient of eye diseases using deep learning.
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Prittopaul, P., Usha, M., Thirumalai, N., Vasanth, M., Raj Kumar, R., Sakthidhasan, B. (2023). An Optimized Taxonomy and Identification of Retinal Eye Diseases for Diabetes Patients Using CNN. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Infrastructure and Computing. Lecture Notes in Networks and Systems, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-19-5331-6_14
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DOI: https://doi.org/10.1007/978-981-19-5331-6_14
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