Harnessing artificial intelligence for ophthalmic disease diagnosis: a comparative study of CNNs and Swin transformer models

Abstract

Ophthalmic diseases are prevalent worldwide, encompassing a diverse range of conditions such as cataracts, glaucoma, and retinal diseases. The World Health Organization (WHO) reports that more than 2.2 billion individuals globally suffer from visual impairments, with nearly half of these cases being preventable through early diagnosis and timely treatment. However, ophthalmologists encounter multiple challenges in clinical diagnosis, including the complexity of cases, the absence of clear early symptoms leading to delayed detection and intervention, and a scarcity of medical professionals in remote and developing regions. To address these issues, Artificial Intelligence (AI) has emerged as a potential solution. This research aims to explore the application of AI in the classification of three common eye diseases: cataracts, retinal diseases, and glaucoma. Specifically, we compare the performance of six Convolutional Neural Network (CNN) models and Swin-Transformer models using the same dataset. The objectives of this research are twofold: (1) to demonstrate the potential of AI in diagnosing eye diseases, and (2) to determine whether Transformer models outperform CNN models in terms of eye disease classification. To achieve these objectives, we have compiled a comprehensive dataset including images of cataracts, retinal diseases, and glaucoma. Through transfer learning, we trained and evaluated models on this dataset. This study offers valuable insights into the application of AI in diagnosing eye diseases, potentially paving the way for the development of AI tools that can support medical professionals in accurate diagnosis and treatment. Ultimately, the integration of AI in ophthalmology could enhance patient care and outcomes.