Khan Asad Mansoor, Akram Muhammad Usman, Nazir Sajid, Hassan Taimur, Khawaja Sajid Gul, Fatima Tatheer
National University of Sciences and Technology, Islamabad, 44000, Pakistan.
Department of Computing, Glasgow Caledonian University, Glasgow, UK.
Biomed Signal Process Control. 2023 Aug;85:104855. doi: 10.1016/j.bspc.2023.104855. Epub 2023 Mar 24.
Chest X-rays (CXR) are the most commonly used imaging methodology in radiology to diagnose pulmonary diseases with close to 2 billion CXRs taken every year. The recent upsurge of COVID-19 and its variants accompanied by pneumonia and tuberculosis can be fatal in some cases and lives could be saved through early detection and appropriate intervention for the advanced cases. Thus CXRs can be used for an automated severity grading of pulmonary diseases that can aid radiologists in making better and informed diagnoses. In this article, we propose a single framework for disease classification and severity scoring produced by segmenting the lungs into six regions. We present a modified progressive learning technique in which the amount of augmentations at each step is capped. Our base network in the framework is first trained using modified progressive learning and can then be tweaked for new data sets. Furthermore, the segmentation task makes use of an attention map generated within and by the network itself. This attention mechanism allows to achieve segmentation results that are on par with networks having an order of magnitude or more parameters. We also propose severity score grading for 4 thoracic diseases that can provide a single-digit score corresponding to the spread of opacity in different lung segments with the help of radiologists. The proposed framework is evaluated using the BRAX data set for segmentation and classification into six classes with severity grading for a subset of the classes. On the BRAX validation data set, we achieve F1 scores of 0.924 and 0.939 without and with fine-tuning, respectively. A mean matching score of 80.8% is obtained for severity score grading while an average area under receiver operating characteristic curve of 0.88 is achieved for classification.
胸部X光（CXR）是放射学中诊断肺部疾病最常用的成像方法，每年进行的胸部X光检查接近20亿次。最近新冠病毒及其变种的激增，伴随着肺炎和肺结核，在某些情况下可能是致命的，而对于晚期病例，通过早期检测和适当干预可以挽救生命。因此，胸部X光可用于对肺部疾病进行自动严重程度分级，这有助于放射科医生做出更好、更明智的诊断。在本文中，我们提出了一个单一框架，用于通过将肺部划分为六个区域来进行疾病分类和严重程度评分。我们提出了一种改进的渐进学习技术，其中每个步骤的增强量是有上限的。我们框架中的基础网络首先使用改进的渐进学习进行训练，然后可以针对新数据集进行调整。此外，分割任务利用网络自身内部生成的注意力图。这种注意力机制能够实现与参数数量多一个数量级或更多的网络相当的分割结果。我们还为4种胸部疾病提出了严重程度评分分级，借助放射科医生的帮助，可以提供一个与不同肺段中不透明度扩散相对应的个位数评分。所提出的框架使用BRAX数据集进行评估，用于分割和分类为六个类别，并对其中一部分类别进行严重程度分级。在BRAX验证数据集上，我们分别在不进行微调和进行微调的情况下，F1分数达到了0.924和0.939。严重程度评分分级的平均匹配分数为80.8%，而分类的受试者操作特征曲线下平均面积为0.88。
