Abstract
Detection of ocular disorders like glaucoma needs immediate actions in clinical practice to prevent irreversible vision loss. Although several detection methods exist, they usually perform segmentation of optic disc followed by classification to detect glaucoma in clinical fundus images. In such cases, the important visual features are ignored and the classification accuracy is majorly affected by inaccurate segmentation. Further, the deep learning-based existing methods demand the downsampling of fundus images from high-resolution to low-resolution which results in loss of image details, thereby degrading the glaucoma classification performance. To handle these issues, we propose a feature modulating two-stream deep convolutional neural network (CNN). The network accepts the full fundus image and the region of interest (ROI) outlined by clinicians as input to each stream to capture more detailed visual features. A feature modulation technique is also proposed as an intrinsic module in the proposed network to further enrich the feature representation by computing the feature-level correlation between two streams. The proposed model is evaluated on a recently introduced large-scale glaucoma dataset, namely G1020 and has achieved state-of-the-art glaucoma detection performance.
This work is supported by the Science and Engineering Research Board (SERB), Department of Science and Technology, Govt. of India under project No. SRG/2020/001460.
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Majhi, S., Nayak, D.R. (2022). Feature Modulating Two-Stream Deep Convolutional Neural Network for Glaucoma Detection in Fundus Images. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_15
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