Computational intelligence in eye disease diagnosis: a comparative study

Kumar, Sandeep; Gunasundari, R.

doi:10.1007/s11517-022-02737-3

Cited by 4 publications

(2 citation statements)

References 117 publications

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“…Traditional diagnosis methods like slit-lamp examinations by ophthalmologists have limitations due to skill variations and record-keeping issues. However, there is a promising avenue for AMD diagnosis and management through the use of machine learning (ML) algorithms for classifying fundus images 3 .…”

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%

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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show abstract

“…In addition, myopia, another common eye disorder, is related to genetic and environmental factors, and can lead to severe visual complications [2], [3]. In older people, eye problems often go unnoticed due to the gradual growth of symptoms [4]. Also, these chronic eye diseases, if not effectively treated, can lead to visual impairment or even blindness.…”

Section: Introductionmentioning

confidence: 99%

Advances in the diagnosis of ocular diseases: an innovative approach through an expert system

Andrade-Arenas,

Yactayo-Arias

2024

Bulletin EEI

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In the context of ophthalmic care, where early diagnosis of eye disorders plays a crucial role in patients' quality of life, this study focused on the development and evaluation of an expert system based on SWI Prolog. The main objective of this research was to provide an effective method for the preliminary diagnosis of ocular disorders, including cataract, trachoma, uveitis, glaucoma, and presbyopia. For the evaluation of the system, a confusion matrix was implemented and accuracy, sensitivity and specificity were calculated using a sample of 30 cases, of which 20 were positive and 10 negatives. The findings revealed an outstanding accuracy of 95%, with a sensitivity and specificity of 90%. This highlights the potential of the tool as an effective means of early detection of visual problems. In conclusion, this expert system represents a significant advance in ophthalmologic diagnosis, with important implications for clinical care and patients' quality of life, although expansion and validation of the tool in further clinical studies is suggested for its wider and more successful implementation in the field of ophthalmology.

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AMDNet23: Hybrid CNN-LSTM deep learning approach with enhanced preprocessing for age-related macular degeneration (AMD) detection

Ali,

Hossain,

Hossain

et al. 2024

Intelligent Systems with Applications

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Computational intelligence in eye disease diagnosis: a comparative study

Cited by 4 publications

References 117 publications

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

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

Advances in the diagnosis of ocular diseases: an innovative approach through an expert system

AMDNet23: Hybrid CNN-LSTM deep learning approach with enhanced preprocessing for age-related macular degeneration (AMD) detection

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