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Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9.

Nicola Rizzieri¹, Luca Dall'Asta², Maris Ozoliņš^1,3

¹Department of Optometry and Vision Science, Faculty of Physics, Mathematics and Optometry, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia.

Vision (Basel, Switzerland)

|September 23, 2024

Summary

This summary is machine-generated.

State-of-the-art YOLOv8 and YOLOv9 models show promise in segmenting diabetic retinopathy (DR) lesions like microaneurysms from fundus images. While results are acceptable, further research is needed for clinical application.

Keywords:

YOLOv8 YOLOv9 computer vision diabetic retinopathy retinal fundus segmentation

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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 early signs like microaneurysms (MAs), hemorrhages (HEMOs), and exudates (EXs).
Computer vision models are increasingly utilized for automated detection and classification of these early DR indicators in retinal fundus images.

Purpose of the Study:

To evaluate the performance of YOLOv8 and YOLOv9 architectures for segmenting DR-related lesions and the optic disc.
To assess the feasibility of using these models for DR lesion detection without requiring extensive coding or programming expertise.

Main Methods:

Utilized 100 DR fundus images from the MESSIDOR database, manually annotated for pixel segmentation.
Applied data augmentation techniques including tiling, flipping, and rotating to enhance training sample diversity.
Tested various YOLOv8 and YOLOv9 model variants for lesion detection and segmentation.

Main Results:

Achieved acceptable mean average precision (mAP) in detecting DR lesions (MA, HEMO, EX) and the optic disc.
Demonstrated the potential of YOLO architectures for automated analysis of DR fundus images.
Compared performance against other neural network approaches in the literature.

Conclusions:

YOLOv8 and YOLOv9 show promising results for DR lesion segmentation, but are not yet ready for clinical implementation.
Accurate lesion detection is crucial for early and correct diagnosis of diabetic retinopathy.
Future work should focus on improving MA segmentation, image pre-processing, and utilizing standardized datasets.