Tuberculosis (TB) is an infectious disease that primarily affects the lungs. If the TB is left undiagnosed in its early stage, it may affect other body parts leading to sudden death. A posterior anterior chest radiographs are used to diagnose pulmonary TB. This work presents a novel automatic system for the early detection of pulmonary TB. Initially, the Chan-Vese active contour model is generated to segment the lung region from the preprocessed chest x-rays. Different textural, statistical, and morphological features are extracted from the segmented lungs. Finally, the pertinent feature vector is considered for the classification of chest x-rays using the Naïve Bayes classifier (NBC). The NBC is trained using 10-fold cross validation technique. The proposed method achieves an average accuracy, the area under the curve, specificity, and sensitivity as 95.5%, 98%, 93.3%, and 94.6% respectively, when tested on different available datasets. Comparison results reveals that the Naïve Bayes classifier outperforms than the multinomial Naïve Bayes classifier and Bernoulli's Naïve Bayes classifier in classifying the chest x-rays into healthy and TB.

中文翻译：

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使用朴素贝叶斯分类器对肺结核进行基于语义轮廓的胸部 X 射线分割

结核病 (TB) 是一种主要影响肺部的传染病。如果结核病在早期未被确诊，它可能会影响其他身体部位，导致猝死。后前胸片用于诊断肺结核。这项工作提出了一种用于早期检测肺结核的新型自动系统。最初，生成 Chan-Vese 活动轮廓模型以从预处理的胸部 X 光片中分割肺区域。从分割的肺中提取不同的纹理、统计和形态特征。最后，使用朴素贝叶斯分类器 (NBC) 为胸部 X 射线分类考虑相关特征向量。NBC 使用 10 倍交叉验证技术进行训练。所提出的方法实现了平均准确度、曲线下面积、特异性、在不同的可用数据集上进行测试时，灵敏度分别为 95.5%、98%、93.3% 和 94.6%。比较结果表明，朴素贝叶斯分类器在将胸部 X 射线分类为健康和结核病方面优于多项朴素贝叶斯分类器和伯努利朴素贝叶斯分类器。