Machine Learning Algorithms for Hypertensive Retinopathy Detection through Retinal Fundus Images

Mohan, Nithyakalyani; Murugan, R.; Goel, Tripti

doi:10.1201/9781003180593-3

Cited by 13 publications

(1 citation statement)

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“…Traditional ML methods 10 such as random forest (RF), multilevel perceptron (MLP), decision tree (DT), logistic regression (LR), support vector machines (SVM), and K-nearest neighbors (KNN) have shown promising results in previous studies, showing exceptional performance in image classification tasks 11 . Reliable ML-based settings for classifying fundus images into GA, intermediate AMD, normal, wet AMD categories have great potential for clinical application.…”

Section: Introductionmentioning

confidence: 99%

A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images

Abd El-Khalek,

Balaha,

Alghamdi

et al. 2024

Sci Rep

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The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular eye examinations. Age-related macular degeneration (AMD), a prevalent condition in individuals over 45, is a leading cause of vision impairment in the elderly. This paper presents a comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. This is crucial for early detection and precise diagnosis of age-related macular degeneration (AMD), enabling timely intervention and personalized treatment strategies. We have developed a novel system that extracts both local and global appearance markers from fundus images. These markers are obtained from the entire retina and iso-regions aligned with the optical disc. Applying weighted majority voting on the best classifiers improves performance, resulting in an accuracy of 96.85%, sensitivity of 93.72%, specificity of 97.89%, precision of 93.86%, F1 of 93.72%, ROC of 95.85%, balanced accuracy of 95.81%, and weighted sum of 95.38%. This system not only achieves high accuracy but also provides a detailed assessment of the severity of each retinal region. This approach ensures that the final diagnosis aligns with the physician’s understanding of AMD, aiding them in ongoing treatment and follow-up for AMD patients.

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Section: Introductionmentioning

confidence: 99%