Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi-NCR Campus, Modinagar, Ghaziabad, U.P., 201204, India.
ICE Division, Netaji Subhas University of Technology, Delhi, 110078, India.
Biomed Phys Eng Express. 2024 Sep 3;10(6). doi: 10.1088/2057-1976/ad72f8.
This work proposes a novel technique called Enhanced JAYA (EJAYA) assisted Q-Learning for the classification of pulmonary diseases, such as pneumonia and tuberculosis (TB) sub-classes using chest x-ray images. The work introduces Fuzzy lattices formation to handle real time (non-linear and non-stationary) data based feature extraction using Schrödinger equation. Features based adaptive classification is made possible through the Q-learning algorithm wherein optimal Q-values selection is done via EJAYA optimization algorithm. Fuzzy lattice is formed using x-ray image pixels and lattice Kinetic Energy (K.E.) is calculated using the Schrödinger equation. Feature vector lattices having highest K.E. have been used as an input features for the classifier. The classifier has been employed for pneumonia classification (normal, mild and severe) and Tuberculosis detection (presence or absence). A total of 3000 images have been used for pneumonia classification yielding an accuracy, sensitivity, specificity, precision and F-scores of 97.90%, 98.43%, 97.25%, 97.78% and 98.10%, respectively. For Tuberculosis 600 samples have been used. The achived accuracy, sensitivity, specificity, precision and F-score are 95.50%, 96.39%, 94.40% 95.52% and 95.95%, respectively. Computational time are 40.96 and 39.98 s for pneumonia and TB classification. Classifier learning rate (training accuracy) for pneumonia classes (normal, mild and severe) are 97.907%, 95.375% and 96.391%, respectively and for tuberculosis (present and absent) are 96.928% and 95.905%, respectively. The results have been compared with contemporary classification techniques which shows superiority of the proposed approach in terms of accuracy and speed of classification. The technique could serve as a fast and accurate tool for automated pneumonia and tuberculosis classification.
这项工作提出了一种名为增强 JAYA（EJAYA）辅助 Q-学习的新技术，用于使用胸部 X 射线图像对肺炎和肺结核（TB）等肺部疾病进行分类。该工作引入了模糊晶格形成，以使用薛定谔方程处理基于 Schrödinger 方程的实时（非线性和非平稳）数据的特征提取。通过 Q-学习算法实现了基于特征的自适应分类，其中通过 EJAYA 优化算法进行最优 Q 值选择。使用 X 射线图像像素形成模糊晶格，并使用薛定谔方程计算晶格动能（K.E.）。具有最高 K.E.的特征向量晶格用作分类器的输入特征。该分类器已用于肺炎分类（正常、轻度和重度）和肺结核检测（存在或不存在）。总共使用了 3000 张图像进行肺炎分类，准确率、灵敏度、特异性、精度和 F 分数分别为 97.90%、98.43%、97.25%、97.78%和 98.10%。对于肺结核，使用了 600 个样本。实现的准确率、灵敏度、特异性、精度和 F 分数分别为 95.50%、96.39%、94.40%、95.52%和 95.95%。肺炎和 TB 分类的计算时间分别为 40.96 和 39.98 秒。肺炎类（正常、轻度和重度）的分类器学习率（训练准确率）分别为 97.907%、95.375%和 96.391%，而对于肺结核（存在和不存在），则分别为 96.928%和 95.905%。结果与当代分类技术进行了比较，显示了所提出方法在准确性和分类速度方面的优越性。该技术可以作为一种快速准确的自动肺炎和肺结核分类工具。
