CNN Fusion: A Promising Technique for Ophthalmic Disorder Diagnosis

Abstract

Ophthalmic disorders represent a major global health problem leading to visual impairment and even blindness if not detected and treated early. Deep learning is a popular approach where pre-trained models are now often used to diagnose a variety of diseases from different medical images. In order to provide an accurate and reliable categorization of ophthalmic illnesses, this study suggests a fusion or ensemble technique that harnesses the power of pre-trained models like ResNet50, DenseNet and EfficientNet. The proposed ensemble model enhances the performance by incorporating the benefits of three architectural paradigms, guiding the convergence of each one's distinct strengths to achieve improved overall classification result. The ensemble architecture has undertaken inclusive validation on multiple fundus image datasets, showing improved performance over lone models. An accuracy of 92% demonstrates the potential of ensemble model in the categorization of eye diseases like cataract, diabetic retinopathy, glaucoma and normal eyes. In addition to achieving cutting-edge accuracy, the ensemble also offers an AUC-ROC score of 1.00. This technique presents a viable alternative for boosting the accuracy and reliability in identifying various eye illnesses by combining diverse model capabilities. The empirical data and conclusions show that the ensemble model has the potential to make a substantial contribution to the field of ocular medical imaging and patient care by enabling precise and timely treatments that may significantly improve the lives of millions of people worldwide who are impacted by ophthalmic diseases.