Mishra, SS, Mandal, B and Puhan, NB (2019) Multi-level Dual-attention Based CNN for Macular Optical Coherence Tomography Classification. IEEE Signal Processing Letters, 26 (12). pp. 1793-1797. ISSN 1558-2361

[thumbnail of 21stOct2019.pdf]
21stOct2019.pdf - Accepted Version

Download (1MB) | Preview


In this letter, we propose a multi-level dual-attention model to classify two common macular diseases, age-related macular degeneration (AMD) and diabetic macular edema (DME) from normal macular eye conditions using optical coherence tomography (OCT) imaging technique. Our approach unifies the dual-attention mechanism at multi-levels of the pre-trained deep convolutional neural network (CNN). It provides a focused learning mechanism by taking into account both multi-level features based attention focusing on the salient coarser features and self-attention mechanism attending higher entropy regions of the finer features. Our proposed method enables the network to automatically focus on the relevant parts of the input images at different levels of feature subspaces. This leads to a more locally deformation-aware feature generation and classification. The proposed approach does not require pre-processing steps such as extraction of region of interest, denoising and retinal flattening, making the network more robust and fully automatic. Experimental results on two macular OCT databases show the superior performance of our proposed approach as compared to the current state-of-the-art methodologies.

Item Type: Article
Additional Information: © IEEE 2019. This is the accepted author manuscript (AAM). The final published version (version of record) is available online via IEEE at https://doi.org/10.1109/LSP.2019.2949388 - please refer to any applicable terms of use of the publisher.
Uncontrolled Keywords: attention mechanism, age-related macular degeneration, diabetic macular edema, multi-level dual-attention, optical coherence tomography
Subjects: R Medicine > R Medicine (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 25 Oct 2019 13:24
Last Modified: 12 Feb 2020 10:26
URI: https://eprints.keele.ac.uk/id/eprint/7087

Actions (login required)

View Item
View Item