Ahalya R K, Snekhalatha U, Dhanraj Varun
Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai, India.
Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai, India.
J Therm Biol. 2023 Jan;111:103404. doi: 10.1016/j.jtherbio.2022.103404. Epub 2022 Dec 5.
The aims and objectives of the study were to i) perform image segmentation using a color-based k-means clustering algorithm and feature extraction using binary robust invariant scalable key points (BRISK), maximum stable extremal regions (MSER), features from accelerated segment test (FAST), Harris, and orientated FAST and rotated BRIEF (ORB); ii) compare the performance of classical machine learning techniques such as LogitBoost, Bagging, and SVM with a quantum machine learning technique. For the proposed study, 240 hand thermal images were acquired in the dorsal view and ventral view of both the right and left-hand regions of RA and normal subjects. The hot spot regions from the thermograms were segmented using a color-based k-means clustering technique. The features from the segmented hot spot region were extracted using different feature extraction methods. Finally, normal and RA groups were categorized using LogitBoost, Bagging, and support vector machine (SVM) classifiers. The proposed study used two testing methods, such as 10-fold cross-validation and a percentage split of 80-20%. The LogitBoost classifier outperformed with an accuracy of 93.75% using the 10-fold cross-validation technique compared to other classifiers. Also, the quantum support vector machine (QSVM) obtained a prediction accuracy of 92.7%. Furthermore, the QSVM model reduces the computational cost and training time of the model to classify the RA and normal subjects. Thus, thermograms with classical machine learning and quantum machine learning algorithms could be considered a feasible technique for classifying normal and RA groups.
i）使用基于颜色的k均值聚类算法进行图像分割，并使用二进制稳健不变可缩放关键点（BRISK）、最大稳定极值区域（MSER）、加速段测试特征（FAST）、哈里斯（Harris）以及定向FAST和旋转BRIEF（ORB）进行特征提取；ii）将诸如LogitBoost、Bagging和支持向量机（SVM）等经典机器学习技术的性能与量子机器学习技术进行比较。对于本研究，在类风湿性关节炎（RA）患者和正常受试者的右手和左手区域的背侧视图和腹侧视图中采集了240张手部热图像。使用基于颜色的k均值聚类技术对热成像图中的热点区域进行分割。使用不同的特征提取方法从分割后的热点区域中提取特征。最后，使用LogitBoost、Bagging和支持向量机（SVM）分类器对正常组和RA组进行分类。本研究使用了两种测试方法, 如10折交叉验证和80 - 20%的百分比分割。与其他分类器相比，LogitBoost分类器使用10折交叉验证技术时的准确率达到93.75%，表现最佳。此外，量子支持向量机（QSVM）的预测准确率为92.7%。此外，QSVM模型降低了对RA患者和正常受试者进行分类的模型的计算成本和训练时间。因此，结合经典机器学习和量子机器学习算法的热成像图可被视为对正常组和RA组进行分类的一种可行技术。
