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A New Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO

Carlos Santos1,2, Marilton Aguiar2, Daniel Welfer3

  • 1Computer Center, Federal Institute of Education, Science and Technology Farroupilha, Alegrete 97555-000, Brazil.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational approach for early Diabetic Retinopathy detection. The method effectively identifies fundus lesions, improving diagnostic accuracy and patient outcomes.

Keywords:
Diabetic RetinopathyYOLOdeep learningfundus imageslesions detection

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Area of Science:

  • Ophthalmology
  • Computer Vision
  • Medical Imaging

Background:

  • Diabetic Retinopathy (DR) is a leading cause of vision loss, characterized by fundus lesions like microaneurysms and hemorrhages in early stages.
  • Early detection of these lesions is crucial for preventing severe vision impairment and guiding treatment.
  • Automated detection of DR lesions faces challenges including lesion variability, image quality issues, and difficulties in identifying small objects with deep learning.

Purpose of the Study:

  • To develop and evaluate a novel computational approach for assisting in the medical diagnosis of Diabetic Retinopathy fundus lesions.
  • To overcome challenges in automated DR lesion detection using advanced image processing and deep learning techniques.

Main Methods:

  • The study employed a combination of image processing techniques, data augmentation, transfer learning, and deep neural networks.
  • The proposed approach was implemented using the YOLOv5 model within the PyTorch framework.
  • Training and testing were conducted on the public DDR and IDRiD Diabetic Retinopathy datasets.

Main Results:

  • The proposed approach achieved an mAP of 0.2630 (IoU limit 0.5) and an F1-score of 0.3485 in the validation stage on the DDR dataset.
  • In the test stage on the DDR dataset, the approach yielded an mAP of 0.1540 (IoU limit 0.5) and an F1-score of 0.2521.
  • Experimental results indicate superior performance compared to existing methods for Diabetic Retinopathy lesion detection.

Conclusions:

  • The developed computational approach shows promise for enhancing the early diagnosis of Diabetic Retinopathy.
  • The integration of image processing, data augmentation, and deep learning (YOLOv5) effectively addresses challenges in automated fundus lesion detection.
  • This method offers a valuable tool for DR screening and treatment planning, potentially improving patient vision outcomes.