Automated TB classification using ensemble of deep architectures
Automated TB classification using ensemble of deep architectures
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DOI：
10.1007/s11042-019-07984-5
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发表时间：
2019-11-01
影响因子：
3.6
通讯作者：
Sofat, Sanjeev
中科院分区：
文献类型：
--
作者：
Hooda, Rahul;Mittal, Ajay;Sofat, Sanjeev
结核病（TB）是一种主要影响肺部的传染病。其最初的筛查主要是使用胸片，这也是世界卫生组织推荐的。为了帮助放射科医生诊断这种疾病，不同的计算机辅助诊断（CAD）系统被开发出来。然而，这些系统的开发仍处于早期阶段，因为自动检测结核病极具挑战性。这是由于结核病对CXR造成的影响存在极端差异。在本研究中，提出了一种基于深度学习的结核病检测系统，该系统具有很高的准确性。该方法是AlexNet、GoogleNet和ResNet三种标准体系结构的集成。这项研究的重要贡献在于从头开始训练这些体系结构，并创建一个适合进行结核病分类的集成。所提出的方法在使用公开可用的标准数据集形成的组合数据集上进行训练和评估。该集合的精度为88.24%，曲线下面积为0.93，使大多数现有方法的性能黯然失色。
Tuberculosis (TB) is an infectious disease that mainly affects the lung region. Its initial screening is mostly performed using chest radiograph, which is also recommended by the World Health Organization. To help the radiologists in diagnosing this disease, different computer-aided diagnosis (CAD) systems have been developed. However, the development of these systems are still in the early phases as it is extremely challenging to automatically detect TB. This is due to extreme variations in the impact caused by TB on the CXR. In this study, a deep-learning-based TB detection system has been presented which achieves significantly high accuracy. The proposed method is an ensemble of three standard architectures namely AlexNet, GoogleNet and ResNet. The significant contribution of the study is to train these architectures from scratch and creating an ensemble suited to perform TB classification. The proposed method is trained and evaluated on a combined dataset formed using publicly available standard datasets. The ensemble attains the accuracy of 88.24% and area under the curve is equal to 0.93, which eclipses the performance of most of the existing methods.