Diabetic Retinopathy Detection Using Deep Learning Multistage Training Method

Guefrachi, Sarra; Echtioui, Amira; Hamam, Habib

doi:10.1007/s13369-024-09137-9

Arab J Sci Eng

2024

DOI: 10.1007/s13369-024-09137-9

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Diabetic Retinopathy Detection Using Deep Learning Multistage Training Method

Sarra Guefrachi,

Amira Echtioui,

Habib Hamam

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2024

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Cited by 3 publications

References 39 publications

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An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina

Xu,

He,

2024

Sci Rep

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An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina

Xu,

He,

2024

Sci Rep

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Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification

Ozsari,

Kumru,

Ekinci

et al. 2024

Sensors

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This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as Amanita pantherina, Boletus edulis, Cantharellus cibarius, Lactarius deliciosus, Pleurotus ostreatus and Tricholoma terreum were chosen based on their ecological importance and distinct morphological characteristics. The research employed 5 different machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, and swin transformers, to evaluate their performance in identifying fungi from images. The DenseNet121 model demonstrated the highest accuracy (92%) and AUC score (95%), making it the most effective in distinguishing between species. The study also revealed that transformer-based models, particularly the swin transformer, were less effective, suggesting room for improvement in their application to this task. Further advancements in macrofungi classification could be achieved by expanding datasets, incorporating additional data types such as biochemical, electron microscopy, and RNA/DNA sequences, and using ensemble methods to enhance model performance. The findings contribute valuable insights into both the use of deep learning for biodiversity research and the ecological conservation of macrofungi species.

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FedDL: personalized federated deep learning for enhanced detection and classification of diabetic retinopathy

Bhulakshmi,

Rajput

2024

PeerJ Computer Science

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Diabetic retinopathy (DR) is a condition that can lead to vision loss or blindness and is an unavoidable consequence of diabetes. Regular eye examinations are essential to maintaining a healthy retina and avoiding eye damage. In developing countries with a shortage of ophthalmologists, it is important to find an easier way to assess fundus photographs taken by different optometrists. Manual grading of DR is time-consuming and prone to human error. It is also crucial to securely exchange patients’ fundus image data with hospitals worldwide while maintaining confidentiality in real time. Deep learning (DL) techniques can enhance the accuracy of diagnosing DR. Our primary goal is to develop a system that can monitor various medical facilities while ensuring privacy during the training of DL models. This is made possible through federated learning (FL), which allows for the sharing of parameters instead of actual data, employing a decentralized training approach. We are proposing federated deep learning (FedDL) in FL, a research paradigm that allows for collective training of DL models without exposing clinical information. In this study, we examined five important models within the FL framework, effectively distinguishing between DR stages with the following accuracy rates: 94.66%, 82.07%, 92.19%, 80.02%, and 91.81%. Our study involved five clients, each contributing unique fundus images sourced from publicly available databases, including the Indian Diabetic Retinopathy Image Dataset (IDRiD). To ensure generalization, we used the Structured Analysis of the Retina (STARE) dataset to train the ResNet50 model in a decentralized learning environment in FL. The results indicate that implementing these algorithms in an FL environment significantly enhances privacy and performance compared to conventional centralized learning methods.

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Diabetic Retinopathy Detection Using Deep Learning Multistage Training Method

Cited by 3 publications

References 39 publications

An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina

An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina

Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification

FedDL: personalized federated deep learning for enhanced detection and classification of diabetic retinopathy

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