Classification of Diabetic Retinopathy using Pre-Trained Deep Learning Models
<|reference_start|>Classification of Diabetic Retinopathy using Pre-Trained Deep Learning Models: Diabetic Retinopathy (DR) stands as the leading cause of blindness globally, particularly affecting individuals between the ages of 20 and 70. This paper presents a Computer-Aided Diagnosis (CAD) system designed for the automatic classification of retinal images into five distinct classes: Normal, Mild, Moderate, Severe, and Proliferative Diabetic Retinopathy (PDR). The proposed system leverages Convolutional Neural Networks (CNNs) employing pre-trained deep learning models. Through the application of fine-tuning techniques, our model is trained on fundus images of diabetic retinopathy with resolutions of 350x350x3 and 224x224x3. Experimental results obtained on the Kaggle platform, utilizing resources comprising 4 CPUs, 17 GB RAM, and 1 GB Disk, demonstrate the efficacy of our approach. The achieved Area Under the Curve (AUC) values for CNN, MobileNet, VGG-16, InceptionV3, and InceptionResNetV2 models are 0.50, 0.70, 0.53, 0.63, and 0.69, respectively.<|reference_end|>
@article{al-kamachy2024classification,
    title={Classification of Diabetic Retinopathy using Pre-Trained Deep Learning
  Models},
    author={Inas Al-Kamachy (Karlstad University, Sweden), Reza Hassanpour
  (Rotterdam University, Netherlands), Roya Choupani (Angelo State University,
  USA)},
    journal={arXiv preprint arXiv:2403.19905},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19905},
    primaryClass={cs.CV cs.AI}
}