Research Article
Diabetic Retinopathy Detection Using Convolutional Neural Networks for Mobile Use
@INPROCEEDINGS{10.1007/978-3-031-32029-3_2, author={Meltem Eseng\o{}n\'{y}l and Anselmo Cardoso de Paiva and Jo\"{a}o Rodrigues and Ant\^{o}nio Cunha}, title={Diabetic Retinopathy Detection Using Convolutional Neural Networks for Mobile Use}, proceedings={Wireless Mobile Communication and Healthcare. 11th EAI International Conference, MobiHealth 2022, Virtual Event, November 30 -- December 2, 2022, Proceedings}, proceedings_a={MOBIHEALTH}, year={2023}, month={5}, keywords={Diabetic Retinopathy Deep Learning Transfer Learning Convolutional Neural Networks Mobile Use}, doi={10.1007/978-3-031-32029-3_2} }- Meltem Esengönül
Anselmo Cardoso de Paiva
João Rodrigues
António Cunha
Year: 2023
Diabetic Retinopathy Detection Using Convolutional Neural Networks for Mobile Use
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-32029-3_2
Abstract
Diabetes has significant effects on the human body, one of which is the increase in the blood pressure and when not diagnosed early, can cause severe vision complications and even lead to blindness. Early screening is the key to overcoming such issues which can have a significant impact on rural areas and overcrowded regions. Mobile systems can help bring the technology to those in need. Transfer learning based Deep Learning algorithms combined with mobile retinal imaging systems can significantly reduce the screening time and lower the burden on healthcare workers. In this paper, several efficiency factors of Diabetic Retinopathy detection systems based on Convolutional Neural Networks are tested and evaluated for mobile applications. Two main techniques are used to measure the efficiency of DL based DR detection systems. The first method evaluates the effect of dataset change, where the base architecture of the DL model remains the same. The second method measures the effect of base architecture variation, where the dataset remains unchanged. The results suggest that the inclusivity of the datasets, and the dataset size significantly impact the DR detection accuracy and sensitivity. Amongst the five chosen lightweight architectures, EfficientNet-based DR detection algorithms outperformed the other transfer learning models along with APTOS Blindness Detection dataset.
