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<p>Age-related macular degeneration is a chronic disease affecting a central area of the retina. Accurate disease identification aids in slowing down the progression of age-related macular degeneration and preserving vision. Various traditional techniques have been developed for effective age-related macular degeneration detection. However, traditional approaches failed to detect and classify the disease accurately and it consumes more time. However, traditional approaches failed to detect and classify age-related macular degeneration accurately. This research paper proposed an efficient model named as Multi-Modal Vision transformer model for the early and accurate prediction of age-related macular degeneration. This study aims to combine information from the Color Fundus Photography and Optical Coherence Tomography streams for performing efficient age-related macular degeneration diagnosis. The input images are needed to be preprocessed to enhance the image quality and make it suitable for further processing. The proposed framework integrated a Cascaded group attention transformer block which extracts the significant features from these modalities effectively. This block has the ability to solve computational complexity issues and attention head redundancy problems. Further, the multi-modal fusion method based on self-attention is introduced for fusing the features from Color Fundus Photography and Optical Coherence Tomography images. This fusion model is trained by applying both standard backpropagation and random gradient descent algorithms. For multi-class classification tasks, the fused features are classified into different classes based on the decision score. To visualize the single-modal and multi-modal output images in a heat map we applied a Class Activation Mapping model. Furthermore, the proposed technique is conducted on the Python platform and the performance is evaluated on different datasets with significant evaluation measures. This technique achieves a higher accuracy of 98.65% and a lower computational time of 13.14 s. From this experimental finding, it’s clear that this study offers an outstanding contribution to early age-related macular degeneration detection.</p>

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Multi-modal fusion and vision transformers for robust early AMD prediction

  • 期刊论文

  • 发表日期：

    2025年5月13日

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  • Akila Annamalai,
  • Durgadevi Palani

摘要

Age-related macular degeneration is a chronic disease affecting a central area of the retina. Accurate disease identification aids in slowing down the progression of age-related macular degeneration and preserving vision. Various traditional techniques have been developed for effective age-related macular degeneration detection. However, traditional approaches failed to detect and classify the disease accurately and it consumes more time. However, traditional approaches failed to detect and classify age-related macular degeneration accurately. This research paper proposed an efficient model named as Multi-Modal Vision transformer model for the early and accurate prediction of age-related macular degeneration. This study aims to combine information from the Color Fundus Photography and Optical Coherence Tomography streams for performing efficient age-related macular degeneration diagnosis. The input images are needed to be preprocessed to enhance the image quality and make it suitable for further processing. The proposed framework integrated a Cascaded group attention transformer block which extracts the significant features from these modalities effectively. This block has the ability to solve computational complexity issues and attention head redundancy problems. Further, the multi-modal fusion method based on self-attention is introduced for fusing the features from Color Fundus Photography and Optical Coherence Tomography images. This fusion model is trained by applying both standard backpropagation and random gradient descent algorithms. For multi-class classification tasks, the fused features are classified into different classes based on the decision score. To visualize the single-modal and multi-modal output images in a heat map we applied a Class Activation Mapping model. Furthermore, the proposed technique is conducted on the Python platform and the performance is evaluated on different datasets with significant evaluation measures. This technique achieves a higher accuracy of 98.65% and a lower computational time of 13.14 s. From this experimental finding, it’s clear that this study offers an outstanding contribution to early age-related macular degeneration detection.