Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation
This work addresses early detection and management of diabetic retinopathy for affected individuals, but it appears incremental as it builds on existing CNN and hybrid approaches.
The paper tackled the problem of improving diabetic retinopathy diagnosis by proposing an ensemble learning technique, achieving a validation accuracy of 99% on the APTOS dataset.
In recent years, the focus is on improving the diagnosis of diabetic retinopathy (DR) using machine learning and deep learning technologies. Researchers have explored various approaches, including the use of high-definition medical imaging, AI-driven algorithms such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). Among all the available tools, CNNs have emerged as a preferred tool due to their superior classification accuracy and efficiency. Although the accuracy of CNNs is comparatively better but it can be improved by introducing some hybrid models by combining various machine learning and deep learning models. Therefore, in this paper, an ensemble learning technique is proposed for early detection and management of DR with higher accuracy. The proposed model is tested on the APTOS dataset and it is showing supremacy on the validation accuracy ($99\%)$ in comparison to the previous models. Hence, the model can be helpful for early detection and treatment of the DR, thereby enhancing the overall quality of care for affected individuals.