@inproceedings{b9f36aac2b8d4c10baae4ceee2c423a7,
title = "Multimodal Deep Learning for Diabetic Retinopathy Grading: Integrating Linear-Radon Sinograms and Retinal Fundus Images",
abstract = "Automated diabetic retinopathy (DR) grading is crucial for disease monitoring and personalized treatment, challenged by high intra-class variation and data imbalance. This paper presents a novel approach to enhancing DR grading detection by integrating linear-Radon sinogram-based images with original retinal images to provide a multimodal network using different convolutional neural network (CNN) architectures. Using the Kaggle Aptos dataset, we evaluated the performance of this multimodal integration. Our findings reveal a significant improvement in multi-class classification performance compared to unimodal retina-only images, underscoring our method's ability to detect subtle patterns among different DR grades.",
keywords = "Convolutional neural networks, Feature extraction, Radon Transform, Retinopathy detection, Transfer learning",
author = "Farida Mohsen and Uzair Shah and Ashhadul Islam and Zubair Shah and Belhaouari, \{Samir Brahim\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 26th IEEE International Workshop on Multimedia Signal Processing, MMSP 2024 ; Conference date: 02-10-2024 Through 04-10-2024",
year = "2024",
month = oct,
day = "4",
doi = "10.1109/MMSP61759.2024.10743640",
language = "English",
series = "2024 IEEE 26th International Workshop on Multimedia Signal Processing, MMSP 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE 26th International Workshop on Multimedia Signal Processing, MMSP 2024",
address = "United States",
}