Cardiovascular diseases (CVDs) are a leading cause of mortality
worldwide. Accurate and early diagnosis is critical for effective
treatment. Traditional methods of medical imaging analysis often lack
precision and efficiency. The challenge lies in enhancing the accuracy
and efficiency of medical imaging analysis for CVD diagnosis using
advanced computational methods. This study proposes a novel
approach that integrates extreme learning machines (ELM) for feature
extraction with neuro-fuzzy systems for classification. The ELMs
efficiently extract relevant features from medical images, while the
neuro-fuzzy systems classify these features with high accuracy.
Experimental results demonstrate a significant improvement in
diagnosis accuracy. The proposed method achieved a classification
accuracy of 95.7%, sensitivity of 94.3%, and specificity of 96.2%. These
results outperform several existing methods in terms of both accuracy
and computational efficiency.
H. Summia Parveen1, S. Karthik2, M.S. Kavitha3, R. Sabitha4
Sri Eshwar College of Engineering, India1, SNS College of Technology, India2,3,4
Cardiovascular Disease, Medical Imaging, Extreme Learning Machines, Neuro-Fuzzy Systems, Feature Extraction
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 15 , Issue: 1 , Pages: 3465 - 3472 )
Date of Publication :
July 2024
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328
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