Muhammad Mohsin, Butt and Dayang Nurfatimah, Awang Iskandar and Sherif E., Abdelhamid and Ghazanfar, Latif and Runna, Alghazo (2022) Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features. Diagnostic, 12 (7). pp. 1-17. ISSN 2075-4418
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Abstract
Diabetic Retinopathy (DR) is a medical condition present in patients suffering from long-term diabetes. If a diagnosis is not carried out at an early stage, it can lead to vision impairment. High blood sugar in diabetic patients is the main source of DR. This affects the blood vessels within the retina. Manual detection of DR is a difficult task since it can affect the retina, causing structural changes such as Microaneurysms (MAs), Exudates (EXs), Hemorrhages (HMs), and extra blood vessel growth. In this work, a hybrid technique for the detection and classification of Diabetic Retinopathy in fundus images of the eye is proposed. Transfer learning (TL) is used on pre-trained Convolutional Neural Network (CNN) models to extract features that are combined to generate a hybrid feature vector. This feature vector is passed on to various classifiers for binary and multiclass classification of fundus images. System performance is measured using various metrics and results are compared with recent approaches for DR detection. The proposed method provides significant performance improvement in DR detection for fundus images. For binary classification, the proposed modified method achieved the highest accuracy of 97.8% and 89.29% for multiclass classification.
Item Type: | Article |
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Uncontrolled Keywords: | hybrid deep learning features; fundus images; diabetic retinopathy; convolutional neural network features. |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology Faculties, Institutes, Centres > Faculty of Computer Science and Information Technology Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology |
Depositing User: | Awg Iskandar |
Date Deposited: | 22 Aug 2024 04:40 |
Last Modified: | 22 Aug 2024 04:40 |
URI: | http://ir.unimas.my/id/eprint/45750 |
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