Integrating AI in Ophthalmology: A Deep Learning Approach for Automated Ocular Toxoplasmosis Diagnosis

Background: Ocular Toxoplasmosis, a leading cause of Posterior Uveitis, demands timely diagnosis to prevent vision loss. Manual retinal image analysis is labor-intensive and variable, while existing Deep Learning models often fail to balance local details and global context in Medical Image Classification. Objective: I propose RetinaCoAt, a Hybrid Deep Learning Model based on the CoAtNet Architecture, for Automated Diagnosis of Ocular Toxoplasmosis, integrating local and global features in Retinal Image Analysis. Methods: RetinaCoAt combines Convolutional Neural Networks (CNNs) for local pathological pattern detection with Transformer Models using multi-head self-attention for global context. Enhanced by residual connections and optimized tokenization, it was trained on 3,659 retinal images (healthy vs. unhealthy) and benchmarked against VGG16, CNNs, and ResNet. Results: RetinaCoAt achieved 98% accuracy in Medical Image Classification, outperforming VGG16 (96.87%), CNNs (95%), and ResNet (93.75%), due to its robust CNN-Transformer synergy. Conclusion: RetinaCoAt advances Automated Diagnosis of Ocular Toxoplasmosis and Posterior Uveitis, with potential for broader retinal pathology detection.

@article{Alawfi2025,
  title     = {Integrating AI in Ophthalmology: A Deep Learning Approach for Automated Ocular Toxoplasmosis Diagnosis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Bader S. Alawfi},
  doi       = {10.14569/IJACSA.2025.0160574},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160574}
}