Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/3882
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 ROC Xception |
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 |
URI: | http://hdl.handle.net/2080/3882 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
2022_CSITSS_MSandeep_ADeep.pdf | 194.14 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.