A contrastive study of analyzing the proficiency of different neural networks ‎for ocular diagnosis - Deshmukh, Prathamesh; Mali , Shubham; Khandekar , Raj; Pagar, Surbhi; Sahane, Reena (International Journal of Advanced Mathematical Sciences, 2025)
Nowadays, eye diseases are becoming common and diagnosing them quickly and instantly can save the ‎patient'sd eyes otherwise it can lead to permanent blindness. This study compares the performance of five new ‎modern advanced neural networks that are not widely used - ConvNeXt, Swin Transformer, CoAtNet, ‎LeViT, and EfficientFormer in detecting patient eye disease. By comparing these models with each other, we ‎aim to find the most effective and accurate model for detecting eye diseases. This comprehensive study ‎undertakes an exhaustive examination of various machine learning models trained on an eye disease dataset. ‎Through a meticulous comparative analysis, we assessed these models' relative efficacies and accuracies. Our investigation aims to elucidate which architectural design performs optimally in classifying ‎ocular pathologies, thereby contributing to the advancement of more precise and expeditious diagnostic ‎modalities for eye disorders. Our research endeavors to identify the most effective neural network ‎configuration for automated eye disease classification. By conducting this in-depth comparative study, we ‎aspire to provide valuable insights into the field of medical image analysis. Our findings hold the potential to ‎inform the development of more accurate, efficient, and reliable diagnostic tools in ophthalmology. ‎Ultimately, this study seeks to enhance the quality of patient care by facilitating faster and more precise ‎diagnoses, as well as promoting early detection of ocular diseases. Our research contributes to the growing ‎body of literature on artificial intelligence applications in medical diagnostics. By systematically comparing ‎various architectures, we provide a nuanced understanding of their relative merits in addressing complex ‎visual recognition tasks in ophthalmology. This study serves as a foundation for future investigations aimed at ‎optimizing AI-driven diagnostic tools for improved patient outcomes in eye care.