Prajapati Nisha K, Patel Amitkumar, Mewada Hiren
Government Engineering College, Gandhinagar, Gujarat, India.
V T Patel Department of Electronics and Communication Engineering, Chandubhai S Patel Institute of Technology, Charotar University of Science and Technology (CHARUSAT), Changa, Gujarat, India.
Med Biol Eng Comput. 2025 Jan;63(1):213-227. doi: 10.1007/s11517-024-03190-0. Epub 2024 Sep 18.
Atherosclerosis causes heart disease by forming plaques in arterial walls. IVUS imaging provides a high-resolution cross-sectional view of coronary arteries and plaque morphology. Healthcare professionals diagnose and quantify atherosclerosis physically or using VH-IVUS software. Since manual or VH-IVUS software-based diagnosis is time-consuming, automated plaque characterization tools are essential for accurate atherosclerosis detection and classification. Recently, deep learning (DL) and computer vision (CV) approaches are promising tools for automatically classifying plaques on IVUS images. With this motivation, this manuscript proposes an automated atherosclerotic plaque classification method using a hybrid Ant Lion Optimizer with Deep Learning (AAPC-HALODL) technique on IVUS images. The AAPC-HALODL technique uses the faster regional convolutional neural network (Faster RCNN)-based segmentation approach to identify diseased regions in the IVUS images. Next, the ShuffleNet-v2 model generates a useful set of feature vectors from the segmented IVUS images, and its hyperparameters can be optimally selected by using the HALO technique. Finally, an average ensemble classification process comprising a stacked autoencoder (SAE) and deep extreme learning machine (DELM) model can be utilized. The MICCAI Challenge 2011 dataset was used for AAPC-HALODL simulation analysis. A detailed comparative study showed that the AAPC-HALODL approach outperformed other DL models with a maximum accuracy of 98.33%, precision of 97.87%, sensitivity of 98.33%, and F score of 98.10%.
动脉粥样硬化通过在动脉壁上形成斑块引发心脏病。血管内超声（IVUS）成像可提供冠状动脉及斑块形态的高分辨率横截面视图。医疗保健专业人员通过物理方法或使用VH-IVUS软件来诊断和量化动脉粥样硬化。由于基于手动或VH-IVUS软件的诊断耗时，因此自动化斑块特征分析工具对于准确检测和分类动脉粥样硬化至关重要。最近，深度学习（DL）和计算机视觉（CV）方法是自动对IVUS图像上的斑块进行分类的有前景的工具。出于这一动机，本手稿提出了一种在IVUS图像上使用混合蚁狮优化器与深度学习（AAPC-HALODL）技术的动脉粥样硬化斑块自动分类方法。AAPC-HALODL技术使用基于更快区域卷积神经网络（Faster RCNN）的分割方法来识别IVUS图像中的病变区域。接下来，ShuffleNet-v2模型从分割后的IVUS图像中生成一组有用的特征向量，并且可以使用HALO技术对其超参数进行优化选择。最后，可以利用由堆叠自动编码器（SAE）和深度极限学习机（DELM）模型组成的平均集成分类过程。2011年医学图像计算与计算机辅助干预国际会议（MICCAI）挑战赛数据集用于AAPC-HALODL模拟分析。详细的对比研究表明，AAPC-HALODL方法优于其他DL模型，其最大准确率为98.33%，精确率为97.87%，灵敏度为98.33%，F分数为98.10%。
