Tuberculosis (TB) is still recognized as one of the leading causes of death worldwide. Recent advances in deep learning (DL) have shown to enhance radiologists' ability to interpret chest X-ray (CXR) images accurately and with fewer errors, leading to a better diagnosis of this disease. However, little work has been done to develop models capable of diagnosing TB that offer good performance while being efficient, fast and computationally inexpensive. In this work, we propose LightTBNet, a novel lightweight, fast and efficient deep convolutional network specially customized to detect TB from CXR images. Using a total of 800 frontal CXR images from two publicly available datasets, our solution yielded an accuracy, F1 and area under the ROC curve (AUC) of 0.906, 0.907 and 0.961, respectively, on an independent test subset. The proposed model demonstrates outstanding performance while delivering a rapid prediction, with minimal computational and memory requirements, making it highly suitable for deployment in handheld devices that can be used in low-resource areas with high TB prevalence. Code publicly available at https://github.com/dani-capellan/LightTBNet.

翻译：肺结核（TB）仍被视为全球主要致死原因之一。深度学习（DL）的最新进展已证明能够提升放射科医生解读胸部X光（CXR）图像的准确性并减少误诊，从而改善该疾病的诊断效果。然而，目前针对兼具高性能、高效率、快速且计算成本低的肺结核诊断模型开发工作仍显不足。本研究提出LightTBNet——一种专为从CXR图像中检测肺结核而定制的新型轻量级、快速且高效的深度卷积网络。通过使用来自两个公开数据集的共800张正面CXR图像，本方案在独立测试子集上分别取得了0.906的准确率、0.907的F1分数及0.961的ROC曲线下面积（AUC）。所提模型在实现快速预测的同时展现出卓越性能，且计算和内存需求极低，使其非常适合部署于手持设备，可用于肺结核高发的低资源地区。代码公开获取地址：https://github.com/dani-capellan/LightTBNet。