Key result
The ODTL-APCIUI deep learning model achieved a maximum classification accuracy of 97.19%, outperforming existing models for detecting atherosclerotic plaque and calcification on intravascular ultrasound images.
Why the study?
Although IVUS plaque classification is essential for CAD risk stratification, manual frame-by-frame analysis of entire vascular segments is labor intensive.
Does the ODTL-APCIUI deep learning technique improve the classification of atherosclerotic plaque and calcification on IVUS images compared to existing models?
Does the ODTL-APCIUI deep learning technique improve the classification of atherosclerotic plaque and calcification on IVUS images compared to existing models?
Absolute Event Rate: 97.19% vs 94.02%
The proposed ODTL-APCIUI deep learning technique demonstrates high accuracy in automatically classifying atherosclerotic plaque and calcification on IVUS images, potentially aiding in risk stratification and clinical decision-making.
May support automated IVUS plaque analysis in research; leaves open prospective validation before clinical use.
Intravascular ultrasound (IVUS) is suitable for evaluating plaque and lesion morphology features and helps to make clinical decisions during the treatment of coronary artery disease (CAD).IVUS remain a gold standard in accessing atherosclerosis plaques, coronary lesions, and stenosis.Even though plaque classification by IVUS is essential for risk stratification, frame-by-frame analysis of an entire vascular segment is labor intensive.In recent times, in the field of deep learning (DL), convolutional neural network (CNN) has been intended to adaptively and automatically determine the spatial hierarchy through backpropagation.The study develops an optimal deep transfer learning based on atherosclerotic plaque and calcification on intravascular ultrasound images ( ODTL-APCIUI) technique. The presented ODTL-APCIUI technique aims to classify atherosclerotic plaque and calcification. To accomplish this, the presented ODTL-APCIUI technique preprocesses the IVUS images by Gaussian filtering (GF)technique.In addition, U2Net model is applied for the segmentation process with Adam optimizer based hyperparameter tuning.Moreover, the ODTL-APCIUI technique uses DenseNet-169 model for feature extraction purposes.Furthermore, the ODTL-APCIUI technique exploits stacked autoencoder (SAE) for classification process.Finally, Harris Hawks optimization (HHO) algorithm is exploited for the hyperparameter adjustment of the SAE approach.The performance assessment of ODTL-APCIUI algorithm is tested using medical images and the results are investigated under different metrics.The experimental outcomes demonstrated that the ODTL-APCIUI technique has gained better performance with maximum accuracy of 97.19%, precision of 94.09%, sensitivity of 97.17%, specificity of 97.19%, and F-score of 92.52%.
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A 2023 study studied Atherosclerotic plaque and calcification (n=10). ODTL-APCIUI deep learning model vs. Existing machine learning and deep learning models was evaluated on Classification accuracy. The ODTL-APCIUI deep learning model achieved a maximum classification accuracy of 97.19%, outperforming existing models for detecting atherosclerotic plaque and calcification on intravascular ultrasound images.
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