Key result
The AIWO-DN technique achieved a maximum classification accuracy of 95.32% at 100 epochs, outperforming existing machine learning models for coronary atherosclerosis plaque detection.
Why the study?
Existing automated coronary plaque recognition models mainly rely on handcrafted features, prompting the need for deep learning models to improve plaque detection and classification.
Does the AIWO-DN deep learning model improve the detection and classification of coronary atherosclerosis plaques in CTA images compared to existing machine learning models?
Does the AIWO-DN deep learning model improve the detection and classification of coronary atherosclerosis plaques in CTA images compared to existing machine learning models?
The AIWO-DN deep learning model demonstrates high accuracy in detecting and classifying coronary atherosclerotic plaques from CTA images, outperforming several existing machine learning techniques.
May support AI development for plaque detection research; leaves open clinical adoption pending prospective validation.
Coronary artery disease (CAD) remains a major reason for increased mortality over the globe, comprising myocardial infarction and ischemic cardiomyopathy. The CAD is highly linked to coronary stenosis owing to the encumbrance of atherosclerotic plaques. Particularly, diversified atherosclerotic plaques are highly responsible for major cardiac adverse events over the calcified and non-calcified plaques. There, the recognition and classification of atherosclerotic plaques play a vital role to prevent and intervene in CAD. The process of detecting various class labels of the atherosclerotic plaques is significant to identify the disease at the earlier stages. Since several automated coronary plaque recognition models are mainly based on handcrafted features, it is needed to design deep learning (DL) models for improved performance. With this motivation, this study introduces an automated invasive weed optimization with densely connected networks (AIWO-DN) for coronary atherosclerosis plaque recognition and classification. Primarily, the Two Dimensional (2D) transverse cross-sectional image with the provided centreline from the input 3-D Computed Tomography Angiography (CTA) image is extracted in three orthographic aspects. In addition, the coronary lumen is segmented on every cross section and extracts the region of interest (RoI). Moreover, the Densely Connected Networks (DenseNet169) model is applied to derive the useful set of features vectors. Furthermore, invasive weed optimization (IWO) with weighted extreme learning machine (WELM) based classification model is employed to detect and classify different classes of atherosclerotic plaques. In order to validate the performance of the superior outcomes of the Automated Invasive Weed Optimization – Deep Learning (AIWO-DN) technique, a set of simulations were performed and the outcomes are inspected interms of varying metrics. The experimental results showed the betterment of the AIWO-DN technique over the existing techniques interms of several evaluation metrics.
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Deivanayagi et al. (2022) studied Coronary atherosclerosis plaque (n=18). Automated Invasive Weed Optimization with Densely Connected Networks (AIWO-DN) vs. SVM-RaRS, SVM-IFV, RF-RaRS, and RF-IFV models was evaluated on Classification accuracy at 100 epochs. The AIWO-DN technique achieved a maximum classification accuracy of 95.32% at 100 epochs, outperforming existing machine learning models for coronary atherosclerosis plaque detection.
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