AutoML-based neural network for the detection of keratoconus

For many years, the diagnosis of Keratoconus (KCN) has heavily relied upon the expertise and subjective judgment of clinicians. However, this study represents a pioneering effort to revolutionize KCN identification through the integration of Convolutional Neural Network (CNN) classification. In this innovative approach, the model is specifically designed to process corneal images as its primary input data, categorizing them into one of three distinct classes: KCN, suspect, or normal. Remarkably, this CNN model, meticulously implemented on the Edge Impulse platform, has achieved an astounding accuracy rate of 98.2% in correctly identifying KCN cases. Nevertheless, there remains substantial potential for improvement in the accurate classification of the suspect and normal categories. In conclusion, this study exemplifies the vast potential of CNNs to transform the diagnosis of keratoconus. With ongoing refinements, including enhanced data preprocessing, attention mechanisms, and quantitative integration, this approach could herald a transformative era in KCN identification and management.