Transfer Learning for Automated Classification of Eye Disease in Fundus Images from Pretrained Model

In today's environment, diseases are on the rise and manifest in various ways, harming numerous bodily parts. In recent years, sophisticated analysis of retinal pictures has enabled the development of computerised methods for diagnosing various disorders. These tools save us both time and money. The analysis of retinal fundus pictures is essential for the early diagnosis of eye disorders. For years, fundus images have also been utilised to detect retinal disorders. If the two picture modalities were merged, the resulting image would be more useful since fundus images detect abnormalities such as drusen, Ageographic atrophy, and macular haemorrhages. At the same time, OCT scans reveal these abnormalities' specific shapes and locations. Images of the fundus are an essential diagnostic tool for numerous retinal diseases. This research investigates the effectiveness of transfer learning using the pre-trained model for predicting eye diseases from fundus images. In this study, the fundus images dataset is used, which is collected from the GitHub repository. This dataset has multiple classes, i.e., Maculopathy, Healthy, Glaucoma, Retinitis Pigmentosa and Myopia. To perform the classification, a transfer learning-based pre-trained DenseNet-121 model is proposed. Python has been employed for simulations. The results of the experiment show that, when applied to the test data, the suggested model had the highest precision (97%), sensitivity (92%), and particularity (98%) and outperformed the existing model in terms of all performance metrics.