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Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images.
El-Den, Niveen Nasr; Naglah, Ahmed; Elsharkawy, Mohamed; Ghazal, Mohammed; Alghamdi, Norah Saleh; Sandhu, Harpal; Mahdi, Hani; El-Baz, Ayman.
Affiliation
  • El-Den NN; Department of Computer and System Engineering, Faculty of Engineering, Ain Shams University, Cairo, Egypt.
  • Naglah A; Department of Bioengineering, University of Louisville, Louisville, KY, USA.
  • Elsharkawy M; Department of Bioengineering, University of Louisville, Louisville, KY, USA.
  • Ghazal M; Electrical, Computer and Biomedical Engineering Department, College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates.
  • Alghamdi NS; Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
  • Sandhu H; Department of Bioengineering, University of Louisville, Louisville, KY, USA.
  • Mahdi H; Department of Computer and System Engineering, Faculty of Engineering, Ain Shams University, Cairo, Egypt.
  • El-Baz A; Department of Bioengineering, University of Louisville, Louisville, KY, USA. aselba01@louisville.edu.
Sci Rep ; 13(1): 9590, 2023 06 13.
Article in En | MEDLINE | ID: mdl-37311794
ABSTRACT
Age-related Macular Degeneration (AMD), a retinal disease that affects the macula, can be caused by aging abnormalities in number of different cells and tissues in the retina, retinal pigment epithelium, and choroid, leading to vision loss. An advanced form of AMD, called exudative or wet AMD, is characterized by the ingrowth of abnormal blood vessels beneath or into the macula itself. The diagnosis is confirmed by either fundus auto-fluorescence imaging or optical coherence tomography (OCT) supplemented by fluorescein angiography or OCT angiography without dye. Fluorescein angiography, the gold standard diagnostic procedure for AMD, involves invasive injections of fluorescent dye to highlight retinal vasculature. Meanwhile, patients can be exposed to life-threatening allergic reactions and other risks. This study proposes a scale-adaptive auto-encoder-based model integrated with a deep learning model that can detect AMD early by automatically analyzing the texture patterns in color fundus imaging and correlating them to the vasculature activity in the retina. Moreover, the proposed model can automatically distinguish between AMD grades assisting in early diagnosis and thus allowing for earlier treatment of the patient's condition, slowing the disease and minimizing its severity. Our model features two main blocks, the first is an auto-encoder-based network for scale adaption, and the second is a convolutional neural network (CNN) classification network. Based on a conducted set of experiments, the proposed model achieves higher diagnostic accuracy compared to other models with accuracy, sensitivity, and specificity that reach 96.2%, 96.2%, and 99%, respectively.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Wet Macular Degeneration / Macula Lutea Type of study: Diagnostic_studies / Screening_studies Limits: Humans Language: En Journal: Sci Rep Year: 2023 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Wet Macular Degeneration / Macula Lutea Type of study: Diagnostic_studies / Screening_studies Limits: Humans Language: En Journal: Sci Rep Year: 2023 Document type: Article Affiliation country: