Advanced diabetic retinopathy detection with the R-CNN: A unified visual health solution

• Autorzy: Sri Sravya, Valluri , Naga Srinivasu, Parvathaneni , Shafi, Jana , Hołubowski, Waldemar , Zielonka, Adam
• Języki publikacji: EN

Abstrakty

EN

Diabetic retinopathy (DR) can cause blindness and vision impairment. This degenerative eye condition may lead to an irreversible vision loss. The prevalence of vision impairment and blindness caused by DR emphasizes the critical need for better screening and therapy measures. DR aetiology involves persistent hyperglycemia-induced microvascular abnormalities, oxidative stress, inflammatory reactions, and retinal blood flow changes. Common screening methods for retinal issues include fundus photography, OCT, and fluorescein angiography. For those with diabetic macular edema (DME), it is a common cause of vision loss. Our goal is to develop an automated, cost-effective method for identifying diabetic retinal disease specimens. This study introduces a faster R-CNN method for detecting and classifying DR lesions in retinal images. Those are classified across five different classes. An extensive analysis of 88,704 images from a Kaggle dataset indicates the efficiency of the proposed model, with a reasonable accuracy of 98.38%. The proposed method is robust in disease localization and classification tasks and it has outperformed other existing studies in DR recognition. On evaluating cross-datasets in Kaggle and APTOS, the model has yield better results during training and testing phases.

Słowa kluczowe

PL

diagnostyka choroby; retinopatia cukrzycowa; sieć neuronowa konwolucyjna

EN

disease diagnosis; diabetic retinopathy; convolutional neural network

• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2024
• Tom: Vol. 34, no. 4
• Strony: 661--678
• Opis fizyczny: Bibliogr. 42 poz., rys., tab., wykr.

Twórcy

autor

Sri Sravya, Valluri

• Department of Computer Science and Engineering, Prasad V Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, 520007, Andhra Pradesh, India

autor

Naga Srinivasu, Parvathaneni

• Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, 522503, Andhra Pradesh, India
• Department of Teleinformatics Engineering, Federal University of Ceará, 100 Pres. Kennedy, 60455-970, Fortaleza, Brazil

autor

Shafi, Jana

• Department of Computer Engineering and Information, Prince Sattam bin Abdulaziz University, 11991, Wadi Alddawasir, Saudi Arabia

autor

Hołubowski, Waldemar

• Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland

autor

Zielonka, Adam

• Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland,

Bibliografia

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Identyfikatory

DOI

10.61822/amcs-2024-0044