Eye Disease Detection in Retinal Images using Deep Transfer Learning Techniques - S, Rajalakshmi; M, Jassem; Amaan Meer; S, Angel Deborah (INFOCOMP Journal of Computer Science, 2024)
Image processing with the help of machine learning algorithms is breaking barriers in various fields of study, especially in the medical field. Deep learning image classification algorithms have made disease detection based on images in an easy manner thus assists the medical professional to taking quick decisions. This paper discusses various deep learning algorithms and transfer learning methods used to classify various eye diseases. Kaggle Eye dataset is used for this purpose. We compared four deep learning algorithms, namely EfficientNetB3, Inception V3, VGG 19 and Convolutional Neural Network models. Various categories of eye diseases, namely Cataract, Diabetic Retinopathy, Glaucoma are considered for classification with normal eye with the help of the scanned images. The strengths and weaknesses of these models are compared based on Precision, Recall, Accuracy and F1 score. In an identical testing environment, EfficientNet B3 outperforms the other algorithms and provides better accuracy for the classification of eye diseases.