CDAM-Net: Channel shuffle dual attention based multi-scale CNN for efficient glaucoma detection using fundus images
Article
Das, Dipankar, Nayak, Deepak Ranjan, Bhandary, Sulatha V. and Acharya, U. Rajendra. 2024. "CDAM-Net: Channel shuffle dual attention based multi-scale CNN for efficient glaucoma detection using fundus images." Engineering Applications of Artificial Intelligence. 133 (Part E). https://doi.org/10.1016/j.engappai.2024.108454
| Article Title | CDAM-Net: Channel shuffle dual attention based multi-scale CNN for efficient glaucoma detection using fundus images |
|---|---|
| ERA Journal ID | 32032 |
| Article Category | Article |
| Authors | Das, Dipankar, Nayak, Deepak Ranjan, Bhandary, Sulatha V. and Acharya, U. Rajendra |
| Journal Title | Engineering Applications of Artificial Intelligence |
| Journal Citation | 133 (Part E) |
| Article Number | 108454 |
| Number of Pages | 12 |
| Year | 2024 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0952-1976 |
| 1873-6769 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.engappai.2024.108454 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0952197624006122 |
| Abstract | Glaucoma is a typical eye disorder that induces damage to the optic nerve due to increased intraocular pressure. It ultimately leads to partial or complete blindness without clinical reversal. Hence, it is of utmost importance to screen and detect glaucoma at an early stage. Most of the earlier glaucoma diagnosis methods rely on manual feature engineering, which is time-consuming and requires domain experts. Although recent methods, particularly, convolutional neural networks (CNNs) facilitate learning high-level feature representations from fundus images, they need extensive parameters and pose overfitting issues due to insufficient training samples. Further, conventional CNNs often ignore the minute changes in the lesion region. To overcome these issues, we propose a lightweight multi-scale CNN architecture called as CDAM-Net for effective glaucoma identification from retinal fundus images. Additionally, we introduce an attention module called channel shuffle dual attention (CSDA), comprising of a channel attention block, a spatial attention block and a channel shuffle unit, to focus on important regions in the fundus images, thereby extracting class-specific features. The CDAM-Net mainly consists of multi-scale feature representation (MFR) blocks that enable the extraction of multi-scale features from fundus images. Each MFR block is followed by a CSDA module, which further helps enrich the feature representation. The CDAM-Net is evaluated on a retinal fundus image (RFI) dataset containing 1426 fundus images (837 glaucoma and 589 normal), and the results indicate that CDAM-Net yields promising classification performance compared to existing techniques. Also, ablation studies are carried out to test the effectiveness of each component of the CDAM-Net. |
| Keywords | Attention; Glaucoma detection; Multi-scale CNN; CSDA; CDAM-net |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400304. Biomedical imaging |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Malaviya National Institute of Technology, India |
| Manipal Academy of Higher Education, India | |
| School of Mathematics, Physics and Computing |
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