Diagnosis of glaucoma using multi-scale attention block in convolution neural network and data augmentation techniques

Glaucoma is defined as an eye disease leading to vision loss due to the optic nerve damage. It is often asymptomatic, thus, timely diagnosis and treatment is crucial. In this article, we propose a novel approach for diagnosing glaucoma using deep neural networks, trained on fundus images. Our proposed approach involves several key steps, including data sampling, pre-processing, and classification. To address the data imbalance issue, we employ a combination of suitable image augmentation techniques and Multi-Scale Attention Block (MAS Block) architecture in our deep neural network model. The MAS Block is a specific architecture design for CNNs that allows multiple convolutional filters of various sizes to capture features at several scales in parallel. This will prevent the over-fitting problem and increases the detection accuracy. Through extensive experiments with the ACRIMA dataset, we demonstrate that our proposed approach achieves high accuracy in diagnosing glaucoma. Notably, we recorded the highest accuracy (97.18%) among previous studies. The results from this study reveal the potential of our approach to improve early detection of glaucoma and offer more effective treatment strategies for doctors and clinicians in the future. Timely diagnosis plays a crucial role in managing glaucoma since it is often asymptomatic. Our proposed method utilizing deep neural networks shows promise in enhancing diagnostic accuracy and aiding healthcare professionals in making informed decisions. Glaucoma is an eye disease vision loss due to the optic nerve damage. In this study, we propose a novel approach for diagnosing glaucoma using deep neural networks trained on fundus images. Our proposed approach involves several key steps, including data sampling, pre-processing, and classification. image