Amit Kukker, Rajneesh Sharma, Gaurav Pandey, Mohammad Faseehuddin
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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. 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引用次数: 0
摘要
本研究提出了一种名为增强型 JAYA(EJAYA)的新技术,可辅助 Q-Learning 利用胸部 X 光图像对肺炎和肺结核(TB)等肺部疾病进行分类。这项工作引入了模糊网格形成,利用薛定谔方程处理基于特征提取的实时(非线性和非稳态)数据。通过 Q-learning 算法实现了基于特征的自适应分类,其中最佳 Q 值的选择是通过 EJAYA 优化算法完成的。利用 X 射线图像像素形成模糊晶格,并利用薛定谔方程计算晶格动能(K.E.)。具有最高 K.E. 的特征向量晶格被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)。肺炎分类共使用了 3000 幅图像,准确率、灵敏度、特异性、精确度和 F 值分别为 97.90%、98.43%、97.25%、97.78% 和 98.10%。肺结核使用了 600 个样本。准确率、灵敏度、特异性、精确度和 F 分数分别为 95.50%、96.39%、94.40%、95.52% 和 95.95%。肺炎和肺结核分类的计算时间分别为 40.96 秒和 39.98 秒。肺炎类别(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%,肺结核类别(存在和不存在)的分类器学习率(训练准确率)分别为 96.928% 和 95.905%。将结果与当代分类技术进行比较后发现,所提出的方法在准确性和分类速度方面都更胜一筹。该技术可作为肺炎和肺结核自动分类的快速而准确的工具。
Fuzzy lattices assisted EJAYA Q-learning for automated pulmonary diseases classification.
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.
期刊介绍:
BPEX is an inclusive, international, multidisciplinary journal devoted to publishing new research on any application of physics and/or engineering in medicine and/or biology. Characterized by a broad geographical coverage and a fast-track peer-review process, relevant topics include all aspects of biophysics, medical physics and biomedical engineering. Papers that are almost entirely clinical or biological in their focus are not suitable. The journal has an emphasis on publishing interdisciplinary work and bringing research fields together, encompassing experimental, theoretical and computational work.