Publication Date: 2023/12/12
Abstract: Glaucoma is a prevalent eye disease that can lead to irreversible vision loss if not detected and treated early. Image classification techniques that make use of deep learning models have been showing promising results in diagnosing glaucoma.Traditional deep learning models often require large amounts of labeled data to achieve optimal performance.This paper explores the application of attention-based pretrained models for binary classification tasks using small datasets. However, in many real-world scenarios such as obtaining a substantial labeled dataset can be challenging or costly such as in rare diseses such as glaucoma.To address this issue, attention mechanisms have emerged as a powerful technique to enhance the performance of pretrained models by focusing on relevant features and samples. This paper investigates the effectiveness of attentionbased pretrained models in the context of small datasets for binary classification tasks. Experimental results demonstrate that attention mechanisms can significantly improve the performance of pretrained models on limited data, making them a valuable tool for practical applications.
Keywords: Glaucoma, Deep Learning, Convolutional Neural Networks, Pretrained Models, Attention Mechanisms, Image Classification.
DOI: https://doi.org/10.5281/zenodo.10361718
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23NOV2120.pdf
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