Detection of Diabetic Retinopathy using Deep Learning and Transfer Learning Techniques with Oversampling to Address Imbalanced Dataset - Ranđelović, Teodora (Jihočeská univerzita, 2023)
The study aims to develop a system for detecting diabetic retinopathy using deep learning. In this study I have explored transfer learning with four distinct models and addressed the issue of an unbalanced dataset with oversampling. The final experiment achieved a significant improvement in accuracy and quadratic kappa score. The study highlights the potential of deep learning and the importance of addressing dataset imbalances for accurate results.
Detection of Diabetic Retinopathy using Deep Learning and Transfer Learning Techniques with Oversampling to Address Imbalanced Dataset
Keywords: Diabetic Retinopathy; Deep learning; Transfer learning; Convolutional neural network; Image classification; medical imaging; diabetic macular edema; retinal fundus photographs; comparative analysis; oversampling; accuracy; quadratic kappa score; Diabetic Retinopathy; Deep learning; Transfer learning; Convolutional neural network; Image classification; medical imaging; diabetic macular edema; retinal fundus photographs; comparative analysis; oversampling; accuracy; quadratic kappa score