Deep Learning for Automated Screening of Tuberculosis from Indian Chest X-rays: Analysis and Update

Background and Objective: Tuberculosis (TB) is a significant public health issue and a leading cause of death worldwide. Millions of deaths can be averted by early diagnosis and successful treatment of TB patients.Automated diagnosis of TB holds vast potential to assist medical experts in expediting and improving its diagnosis, especially in developing countries like India, where there is a shortage of trained medical experts and radiologists. To date, several deep learning based methods for automated detection of TB from chest radiographs have been proposed. However, the performance of a few of these methods on the Indian chest radiograph data set has been suboptimal, possibly due to different texture of the lungs on chest radiographs of Indian subjects compared to other countries. Thus deep learning for accurate and automated diagnosis of TB on Indian datasets remains an important subject of research. Methods: The proposed work explores the performance of convolutional neural networks (CNNs) for the diagnosis of TB in Indian chest x-ray images. Three different pre-trained neural network models, AlexNet, GoogLenet, and ResNet are used to classify chest x-ray images into healthy or TB infected. The proposed approach does not require any pre-processing technique. Also, other works use pre-trained NNs as a tool for crafting features and then apply standard classification techniques. However, we attempt an end to end NN model based diagnosis of TB from chest x-rays. The proposed visualization tool can also be used by radiologists in the screening of large datasets. Results: The proposed method achieved 93.40% accuracy with 98.60% sensitivity to diagnose TB for the Indian population. Conclusions: The performance of the proposed method is also tested against techniques described in the literature. The proposed method outperforms the state of art on Indian and Shenzhen datasets.

从印度胸部X射线自动筛查结核病的深度学习：分析和更新

背景与目的：结核病是一个重要的公共卫生问题，也是全球范围内的主要死亡原因。结核病患者的早期诊断和成功治疗可以避免数百万例死亡。.. 结核病的自动诊断具有巨大的潜力，可以帮助医学专家加快和改善其诊断，尤其是在印度等发展中国家，那里缺少训练有素的医学专家和放射科医生。迄今为止，已经提出了几种基于深度学习的方法来从胸部X光片中自动检测结核病。但是，这些方法在印度胸部X线照片数据集上的性能表现欠佳，这可能是由于印度受试者的胸部X线照片上的肺部纹理与其他国家相比有所不同。因此，对印度数据集进行准确而自动的结核病诊断的深度学习仍然是研究的重要课题。方法：拟议的工作探讨了卷积神经网络（CNN）在印度胸部X射线图像中诊断结核的性能。三种不同的预训练神经网络模型AlexNet，GoogLenet和ResNet用于将胸部X射线图像分类为健康或结核病感染者。所提出的方法不需要任何预处理技术。另外，其他作品也使用预训练的NN作为制作特征的工具，然后应用标准分类技术。但是，我们尝试从胸部X射线对结核病进行端到端神经网络诊断。建议的可视化工具还可以被放射科医生用于筛选大型数据集。结果：所提出的方法在诊断印度人结核病方面达到了93.40％的准确度和98.60％的敏感性。结论：还对照文献中描述的技术测试了所提出方法的性能。所提出的方法在印度和深圳数据集上的表现优于现有技术。 （阅读更多）