Research Article
Performance Analysis of CNN Models in the Detection and Classification of Diabetic Retinopathy
@INPROCEEDINGS{10.1007/978-3-031-60665-6_21, author={Francisca L\^{u}cio and Vitor Filipe and Lio Gon\`{e}alves}, title={Performance Analysis of CNN Models in the Detection and Classification of Diabetic Retinopathy}, proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings}, proceedings_a={MOBIHEALTH}, year={2024}, month={6}, keywords={Diabetic retinopathy Deep Learning Classification Detection Convolutional neural network (CNN)}, doi={10.1007/978-3-031-60665-6_21} }- Francisca Lúcio
Vitor Filipe
Lio Gonçalves
Year: 2024
Performance Analysis of CNN Models in the Detection and Classification of Diabetic Retinopathy
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-60665-6_21
Abstract
This study focuses on investigating different CNN architectures and assessing their effectiveness in classifying Diabetic Retinopathy, a diabetes-associated disease that ranks among the primary causes of adult blindness. However, early detection can significantly prevent its debilitating consequences. While regular screening is advised for diabetic patients, limited access to specialized medical professionals can hinder its implementation. To address this challenge, deep learning techniques provide promising solutions, primarily through their application in the analysis of fundus retina images for diagnosis.
Several CNN architectures, including MobileNetV2, VGG16, VGG19, InceptionV3, InceptionResNetV2, Xception, DenseNet121, ResNet50, ResNet50V2, and EfficientNet (ranging from EfficientNetB0 to EfficientNetB6), were implemented to assess and analyze their performance in classifying Diabetic Retinopathy. The dataset comprised 3662 Fundus retina images. Prior to training, the networks underwent pre-training using the ImageNet database, with a Gaussian filter applied to the images as a preprocessing step. As a result, the Efficient-Net stands out for achieving the best performance results with a good balance between model size and computational efficiency. By utilizing the EfficientNetB2 network, a model was trained with an accuracy of 85% and a screening capability of 98% for Diabetic Retinopathy. This model holds the potential to be implemented during the screening stages of Diabetic Retinopathy, aiding in the early identification of individuals at risk.
