Detecting and staging diabetic retinopathy in retinal images using multi-branch CNN
Issued Date
2022-01-01
Resource Type
ISSN
26341964
eISSN
22108327
Scopus ID
2-s2.0-85143229267
Journal Title
Applied Computing and Informatics
Rights Holder(s)
SCOPUS
Bibliographic Citation
Applied Computing and Informatics (2022)
Suggested Citation
Kusakunniran W., Karnjanapreechakorn S., Choopong P., Siriapisith T., Tesavibul N., Phasukkijwatana N., Prakhunhungsit S., Boonsopon S. Detecting and staging diabetic retinopathy in retinal images using multi-branch CNN. Applied Computing and Informatics (2022). doi:10.1108/ACI-06-2022-0150 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84330
Title
Detecting and staging diabetic retinopathy in retinal images using multi-branch CNN
Author's Affiliation
Other Contributor(s)
Abstract
Purpose: This paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically. Design/methodology/approach: The proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers. Findings: The proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario. Originality/value: The new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.