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  1. Home
  3. Explainable Neutrosophic Knowledge Distillation Model For Ocular Disease Classification Using Ultra-wide Field Fundus Images.
  1. Home
  3. Explainable Neutrosophic Knowledge Distillation Model For Ocular Disease Classification Using Ultra-wide Field Fundus Images.

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Related Experiment Video

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
07:23

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

Explainable Neutrosophic Knowledge Distillation Model for Ocular Disease Classification Using Ultra-Wide Field Fundus

Nebras Sobahi1, Muhammed Halil Akpınar2, Salih Taha Alperen Özçelik3

  • 1Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia.

Bioengineering (Basel, Switzerland)
|May 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

An explainable neutrosophic knowledge distillation (NKD) model improves ultra-wide field (UWF) fundus image classification. This AI tool enhances medical screening by accurately identifying retinal diseases, outperforming existing methods.

Keywords:
Grad-CAM++NKDUWF fundus imagingdeep learningexplainable artificial intelligenceretinal disease classification

Related Experiment Videos

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
07:23

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Ultra-wide field (UWF) fundus image classification is crucial for medical screening and decision support.
  • Challenges include class similarity, imbalance, and indeterminate visual patterns in retinal disease classification.
  • Existing methods struggle with the complexity of UWF fundus images.

Purpose of the Study:

  • To propose an explainable neutrosophic knowledge distillation (NKD) model for UWF fundus image classification.
  • To enhance the accuracy and interpretability of AI models in retinal disease detection.
  • To address the limitations of current classification methods for UWF fundus images.

Main Methods:

  • Developed a novel NKD model using a ResNet50 teacher model and a student model.
  • Integrated Contrast Limited Adaptive Histogram Equalization (CLAHE) for preprocessing.
  • Employed neutrosophic distillation to learn from hard labels and teacher model predictions.
  • Utilized Grad-CAM++ for model interpretability analysis.

Main Results:

  • The NKD model achieved a mean accuracy of 84.00% and an F1-score of 84.02% via 5-fold cross-validation.
  • Full model evaluation showed 87.86% accuracy, 97.48% specificity, and 97.48% AUC.
  • Outperformed classical machine learning and baseline CNN models.
  • Grad-CAM++ analysis confirmed the model focused on relevant retinal regions.

Conclusions:

  • The proposed NKD model is effective for UWF fundus image classification.
  • The model offers improved accuracy and interpretability for retinal disease screening.
  • NKD presents a promising approach for complex medical image analysis.