Channel Spatial Attention for automated detection of macular diseases

The performance of a classifier heavily depends on the specific set of features used for the classification task. Accurate classification is crucial in several fields, including engineering and medicine. We propose a feature attention-based technique for automatically recognising macular disorders from retinal Optical Coherence Tomography (OCT) images. By emphasising specific features within regions of interest, the attention function lessens the impact of irrelevant information. Deep convolutional neural networks (CNNs) often incorporate attention methods that combine channel and spatial knowledge to improve model performance. However, these approaches rely on 2D global pooling operations or scaling techniques, which can lead to information loss. We introduce a Channel Spatial Attention approach to capture cross-dimensional interactions, thereby mitigating information loss. This approach achieves significant performance improvements with minimal computational overhead and can seamlessly integrate into any CNN. We validated our proposed strategy using the UCSD benchmark dataset to affirm its significance. The study's results confirm the efficacy and usefulness of our approach in detecting macular diseases from retinal OCT images.