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Related Experiment Video

Updated: Jan 15, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.7K

Revolutionizing AMD detection Bi model CNNs and hybrid feature selection for automated grading.

Jamal Alsamri1, Mohammad Alamgeer2, Ali Alqazzaz3

  • 1Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Scientific Reports
|October 13, 2025
PubMed
Summary

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This study introduces an advanced framework for automated age-related macular degeneration (AMD) grading from fundus images, achieving 99.5% accuracy. The Bi-Model CNN model enhances early disease detection and treatment effectiveness.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Age-related macular degeneration (AMD) is a leading cause of vision loss in elderly populations.
  • Automated grading of AMD from fundus images is crucial for early detection and timely intervention.

Purpose of the Study:

  • To develop a comprehensive framework for enhancing fundus image quality and improving automated AMD grading accuracy.
  • To introduce a novel Bi-Model Convolutional Neural Network (CNN) architecture for precise AMD grading.

Main Methods:

  • Image preprocessing using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gamma correction.
  • Hybrid feature selection combining handcrafted and deep learning features.
  • A Bi-Model CNN architecture integrating global and local features from fundus images and patches.
Keywords:
Adaptive contrast enhancement algorithm (CLAHE)Age-Related macular degenerationBi-Model convolutional neural networksFundus imagesGamma correctionHybrid feature selectionPreprocessing techniques

Related Experiment Videos

Last Updated: Jan 15, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.7K

Main Results:

  • The proposed Bi-CNN + Feature Fusion model achieved an accuracy of 99.5%.
  • The model demonstrated high performance with precision (0.995), recall (0.995), and F1-score (0.995).
  • A Cohen's Kappa of 0.990 indicated near-perfect agreement between predicted and actual AMD grades.

Conclusions:

  • The developed framework significantly enhances fundus image quality and automated AMD grading accuracy.
  • The Bi-Model CNN architecture effectively utilizes global and local features for precise AMD detection.
  • This approach promises more effective early identification of AMD, improving patient outcomes.