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Title: A Deep Learning based Hybrid Model for Classification of Diabetic Retinopathy
Authors: Madarapu, Sandeep
Ari, Samit
Mahapatra, Kamalakanta
Keywords: CLAHE
Deep learning
Diabetic retinopthy
Fundus images LDA
Issue Date: Dec-2022
Citation: 6th International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS-2022), RV College of Engineering, Banglore, 21-23 December 2022
Abstract: Diabetic retinopathy (DR) is a complication of diabetes that effects the retina. Swelling of the retinal blood vessels due to excessive sugar in diabetic patients can cause DR, which damages the retina. Diagnosis of DR is tedious and timeconsuming for clinical experts, hence a computer-aided-diagnosis (CAD) tool is required to detect DR automatically. This paper proposes a novel method for detecting multi-class DR using the Xception model and random forest. First, the fundus images are pre-processed with the contrast-limited adaptive histogram equalization (CLAHE) technique to enhance image contrast by removing the embedded noise. This work proposes a hybrid deep convolutional neural network (DCNN) that concatenates the extracted features from various layers of the pre-trained Xception model to improve the performance. The dimensions of the local features are reduced using linear discriminant analysis (LDA). The resulting features are concatenated and utilized for training the random forest for DR classification. The performance of the proposed method is validated on the publicly available APTOS- 2019 database. The experimental results show that the proposed technique is better compared to the state-of-the-art techniques.
Description: Copyright belongs to proceeding publisher
Appears in Collections:Conference Papers

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