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A multi model deep net with an explainable AI based framework for diabetic retinopathy segmentation and

Neeraj Sharma1, Praveen Lalwani2

  • 1School of Computing Sciences and Engineering, VIT Bhopal University, Kothrikalan, Sehore, 466114, Madhya Pradesh, India. neeraj.sharma2019@vitbhopal.ac.in.

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|March 14, 2025
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Summary
This summary is machine-generated.

This study introduces an AI system for diagnosing Diabetic Retinopathy (DR) that overcomes image quality issues. The novel approach significantly improves diagnostic accuracy, aiding in early detection and treatment of DR.

Keywords:
Adaptive Gabor filterDiabetic RetinopathyExplainable AI and Grad CamModified U-NetMulti-folded featuresSelf-Adaptive Northern Goshawk Optimization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Ophthalmology

Background:

  • Diabetic Retinopathy (DR) is a leading cause of vision loss in diabetic patients, necessitating accurate and timely diagnosis.
  • Current AI-driven DR diagnostic systems face challenges with image quality issues like low contrast, noise, and uneven lighting, impacting performance.
  • Effective DR diagnosis is crucial for preventing irreversible vision impairment.

Purpose of the Study:

  • To develop an advanced AI-based system for the accurate diagnosis of Diabetic Retinopathy.
  • To address and overcome the limitations of existing AI models in handling poor-quality retinal images.
  • To enhance the performance of DR detection through improved image processing and classification techniques.

Main Methods:

  • An Adaptive Gabor Filter (AGF) based on a Chaotic Map was developed to enhance image quality.
  • Multi-feature extraction techniques including Local Binary Patterns (LBP), Speeded-Up Robust Features (SURF), and Texture Energy Measurement (TEM) were employed.
  • A classification model incorporating an Attention layer, DenseNet, and an Optimized Gated Recurrent Unit (OGRU) optimized by a Self-Adaptive Northern Goshawk Optimization (SANGO) algorithm was utilized.
  • Grad Cam was used to validate segmentation and classification performance.

Main Results:

  • The proposed system demonstrated high diagnostic accuracy across three datasets: DiaRetDB1 (99.01%), APTOS 2019 (98.98%), and EyePacs (99.12%).
  • Performance was rigorously evaluated using metrics such as IoU, Accuracy, Precision, Recall, F1-Measure, and Dice Similarity Coefficient (DSC).
  • The system proved robust and reliable in handling diverse datasets and image conditions.

Conclusions:

  • The developed AI system effectively addresses image quality challenges in Diabetic Retinopathy diagnosis.
  • The integration of AGF, advanced feature extraction, and a sophisticated classification model significantly boosts diagnostic performance.
  • This AI-driven approach offers a reliable and accurate solution for early DR detection, potentially reducing vision loss in diabetic patients.