HOG-CNN fuses a 26,244-dimensional HOG descriptor with a frozen CNN embedding and reports accuracy up to 98.5% on APTOS, 92.8% on IC-AMD, and 83.9% on ORIGA.
Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends
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abstract
The emergence of artificial intelligence (AI), particularly deep learning (DL), has marked a new era in the realm of ophthalmology, offering transformative potential for the diagnosis and treatment of posterior segment eye diseases. This review explores the cutting-edge applications of DL across a range of ocular conditions, including diabetic retinopathy, glaucoma, age-related macular degeneration, and retinal vessel segmentation. We provide a comprehensive overview of foundational ML techniques and advanced DL architectures, such as CNNs, attention mechanisms, and transformer-based models, highlighting the evolving role of AI in enhancing diagnostic accuracy, optimizing treatment strategies, and improving overall patient care. Additionally, we present key challenges in integrating AI solutions into clinical practice, including ensuring data diversity, improving algorithm transparency, and effectively leveraging multimodal data. This review emphasizes AI's potential to improve disease diagnosis and enhance patient care while stressing the importance of collaborative efforts to overcome these barriers and fully harness AI's impact in advancing eye care.
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HOG-CNN: Integrating Histogram of Oriented Gradients with Convolutional Neural Networks for Retinal Image Classification
HOG-CNN fuses a 26,244-dimensional HOG descriptor with a frozen CNN embedding and reports accuracy up to 98.5% on APTOS, 92.8% on IC-AMD, and 83.9% on ORIGA.