AUTHOR=Anand Vatsala , Koundal Deepika , Alghamdi Wael Y. , Alsharbi Bayan M. 

TITLE=Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 framework

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 7 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1396160

DOI=10.3389/frai.2024.1396160

ISSN=2624-8212

ABSTRACT=Diabetic retinopathy is a condition that affects the retina and causes vision loss due to blood vessel destruction. The retina is the layer of the eye responsible for visual processing and nerve signaling. Diabetic retinopathy causes vision loss, floaters, and sometimes blindness, yet it often has no warning signals in the early stages. Deep learning-based techniques have emerged as viable options for automated illness classification as large-scale medical imaging datasets become more widely available. In order to adapt to medical image analysis tasks, transfer learning makes use of pre-trained models to extract high-level characteristics from natural images. In this research, an intelligent recommendation based fine-tuned EfficientNetB0 model is proposed for the quick and precise assessment for the diagnosis of Diabetic Retinopathy from fundus images that will help ophthalmologists in early diagnosis and detection. The proposed model EfficientNetB0 model is compared with three transfer learning based models namely ResNet152, VGG16 and DenseNet169. The experimental work is carried out using publicly available dataset from Kaggle consisting of 3200 fundus images. Out of all the transfer learning models, EfficientNetB0 has outperformed with the value of accuracy as 0.91, followed by DenseNet169 with the value of accuracy as 0.90. In comparison to other approaches, the proposed intelligent recommendation based fine-tuned EfficientNetB0 approach delivers state-of-the-art performance on the accuracy, recall, precision and F1-score criteria. The system aims to assist ophthalmologists in early detection, potentially alleviating the burden on healthcare units