RT Journal Article SR Electronic T1 Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach JF BMJ Open Ophthalmology JO BMJ Open Ophth FD BMJ Publishing Group Ltd SP e001589 DO 10.1136/bmjophth-2023-001589 VO 9 IS 1 A1 Yaraghi, Shokufeh A1 Khatibi, Toktam YR 2024 UL http://bmjophth.bmj.com/content/9/1/e001589.abstract AB Objective Our objective is to develop a novel keratoconus image classification system that leverages multiple pretrained models and a transformer architecture to achieve state-of-the-art performance in detecting keratoconus.Methods and analysis Three pretrained models were used to extract features from the input images. These models have been trained on large datasets and have demonstrated strong performance in various computer vision tasks.The extracted features from the three pretrained models were fused using a feature fusion technique. This fusion aimed to combine the strengths of each model and capture a more comprehensive representation of the input images. The fused features were then used as input to a vision transformer, a powerful architecture that has shown excellent performance in image classification tasks. The vision transformer learnt to classify the input images as either indicative of keratoconus or not.The proposed method was applied to the Shahroud Cohort Eye collection and keratoconus detection dataset. The performance of the model was evaluated using standard evaluation metrics such as accuracy, precision, recall and F1 score.Results The research results demonstrated that the proposed model achieved higher accuracy compared with using each model individually.Conclusion The findings of this study suggest that the proposed approach can significantly improve the accuracy of image classification models for keratoconus detection. This approach can serve as an effective decision support system alongside physicians, aiding in the diagnosis of keratoconus and potentially reducing the need for invasive procedures such as corneal transplantation in severe cases.Data may be obtained from a third party and are not publicly available. This is a retrospective study. Our considered dataset is a cohort dataset (Shahroud Eye Cohort Study). For collecting this dataset legal process and ethics approval taken legal issues performed. Patients or the public were involved in the design, or conduct, or reporting, or dissemination plans of our research.