Abstract
Macula Edema is observed in many patients having diabetes for more than ten years. It is more so in patients who have fluctuating sugar level or uncontrolled diabetes. In the case of Macula Edema, in spite of being the commonest cause, the patient realizes the issue, only when there is deterioration of vision. Experts use surrogates such as exudates near to fovea in fundus photographs for detection of Macula Edema through clinical examination. The severity is based on the proximity of the exudates to the fovea. In the present scenario with the rising rate of diabetes, an automated technique can act as an aid for the quick detection of the disease and also adds value to healthcare. This paper proposes a morphological method for extraction of exudates. A novel approach is proposed for locating the macula irrespective of the position of optic disc. The overall accuracy obtained for classification is 94.74%. The balanced accuracy obtained for classification of Normal, Non Clinically Significant Macula Edema and Clinically Significant Macula Edema is 97.92%, 92.42% and 96.77%, respectively.
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Prabhu, N., Bhoir, D., Shanbhag, N., Rao, U. (2020). Exudate-Based Classification for Detection of Severity of Diabetic Macula Edema. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1230. Springer, Cham. https://doi.org/10.1007/978-3-030-52243-8_12
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