Abstract
Diabetic Retinopathy is an important problem in the whole world and because of this disease a lot of people are losing their vision. Many processes are available to detect diabetic retinopathy and manual screening is one of those. This work proposes a method consists of three steps, namely image preprocessing, Principal Component Analysis—Scale Invariant Feature Transform (PCA-SIFT) based feature extraction and then classification. After separating noisy dataset by applying adaptive noise detector, PCA-SIFT is used for feature extraction. These features are then fed into the classifiers. Naive Bayes classifier is used for noise free data and for the rest (noisy) data, prior and posterior probability based weighted decision tree is applied for classification. Thus we have achieved superior result even for the noisy dataset which increases the efficiency of the system.
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Johora, F.T., Md. Mahbub -Or-Rashid, Yousuf, M.A., Saha, T.R., Ahmed, B. (2020). Diabetic Retinopathy Detection Using PCA-SIFT and Weighted Decision Tree. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_3
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DOI: https://doi.org/10.1007/978-981-13-7564-4_3
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