Faculty of Electronics & Electrical Engineering, University of Engineering & Technology, Taxila, 47080, Pakistan.
Department of Electronics Engineering, University of Chakwal, Chakwal, Pakistan.
Phys Eng Sci Med. 2021 Mar;44(1):183-194. doi: 10.1007/s13246-020-00966-0. Epub 2021 Jan 18.
Tuberculosis (TB) remains one of the major health problems in modern times with a high mortality rate. While efforts are being made to make early diagnosis accessible and more reliable in high burden TB countries, digital chest radiography has become a popular source for this purpose. However, the screening process requires expert radiologists which may be a potential barrier in developing countries. A fully automatic computer-aided diagnosis system can reduce the need of trained personnel for early diagnosis of TB using chest X-ray images. In this paper, we have proposed a novel TB detection technique that combines hand-crafted features with deep features (convolutional neural network-based) through Ensemble Learning. Handcrafted features were extracted via Gabor Filter and deep features were extracted via pre-trained deep learning models. Two publicly available datasets namely (i) Montgomery and (ii) Shenzhen were used to evaluate the proposed system. The proposed methodology was validated with a k-fold cross-validation scheme. The area under receiver operating characteristics curves of 0.99 and 0.97 were achieved for Shenzhen and Montgomery datasets respectively which shows the superiority of the proposed scheme.
结核病（TB）仍然是现代社会的主要健康问题之一，死亡率很高。虽然各国正在努力提高高负担结核病国家的早期诊断的可及性和可靠性，但数字胸部 X 射线摄影已成为实现这一目标的热门来源。然而，筛查过程需要专家放射科医生，这可能是发展中国家的一个潜在障碍。使用胸部 X 射线图像的全自动计算机辅助诊断系统可以减少对训练有素的人员进行结核病早期诊断的需求。在本文中，我们提出了一种新颖的 TB 检测技术，该技术通过集成学习将手工制作的特征与深度特征（基于卷积神经网络）相结合。手工制作的特征是通过 Gabor 滤波器提取的，深度特征是通过预先训练的深度学习模型提取的。我们使用了两个公开可用的数据集，即（i）蒙哥马利和（ii）深圳，来评估所提出的系统。所提出的方法通过 k 折交叉验证方案进行了验证。深圳和蒙哥马利数据集的接收器操作特性曲线下的面积分别达到 0.99 和 0.97，这表明了所提出方案的优越性。
