Automated Diabetic Retinopathy Diagnosis Using Ensemble Approach

Title

Automated Diabetic Retinopathy Diagnosis Using Ensemble Approach

Creator

Swamy, C. Manjunatha; Kumar, S. Babu; Yogish, Deepa; Kiran, B.

Description

Diabetic Retinopathy is a major reason of vision impairment among diabetic patients, early and accurate diagnosis is crucial. This research focuses on developing a machine learning-based classification system to detect different stages of DR using Support Vector Machine (SVM), Random Forest (RF) and ensemble model. The dataset is divided into five categories: Healthy, Mild, Moderate, Proliferative and Severe DR. Performance evaluation using various metrics, including Accuracy, F1-score, RMSE and AUC-ROC, indicates that the ensemble model achieves the best results, with an accuracy of 77.66% and an AUC-ROC of 0.9015. The confusion matrices show that existing models struggle with certain misclassifications, the ensemble approach enhances overall predictive capability. Future improvements can include integrating deep learning models such as convolutional Neural Networks leveraging larger and more diverse datasets and incorporating image preprocessing techniques to enhance feature extraction. This system can help ophthalmologists to detect early and treatment planning, ultimately decrease the risk of blindness in diabetic patients. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Source

Lecture Notes in Networks and Systems;Volume;1863 LNNS;pp.408-418

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Convolutional Neural Networks; Diabetic Retinopathy; Ensemble Model; Machine Learning; Random Forest; Support Vector Machine

Coverage

Swamy C.M., Department of AI, ML and Data Science, School of Engineering and Technology, Christ University, Bengaluru, India; Kumar S.B., Department of AI, ML and Data Science, School of Engineering and Technology, Christ University, Bengaluru, India; Yogish D., Department of CSE, School of Engineering and Technology, Christ University, Bengaluru, India; Kiran B., Department of CSE, PES University, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-303219181-6;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-19182-3_31

https://www.scopus.com/pages/publications/105039137901?origin=resultslist