Chest X-ray (CXR) images are usually used to identify the causes of patients’ symptoms, including the classes of lung or heart disorders. In visualization examination, CXR imaging in anterior–posterior (A–P) views is a preliminary screening method used by clinicians or radiologists to diagnose possible lung abnormalities, such as pneumothorax (Pt), emphysema (E), infiltration (In), lung cancer (M), pneumonia (P), pulmonary fibrosis (F), and pleural effusion (Ef). However, the identification of the causes of multiple abnormalities associated with coexisting conditions presents a challenge. In ruling out a suspected lung disease, the signs and symptoms of physical conditions need to be identified to arrive at a definitive diagnosis. In addition, low contrast CXR images and manual inspection restrict automated screening applications. Hence, this study aims to propose an iterated function system (IFS) and a multilayer fractional-order machine learning classifier to rapidly screen the possible classes of lung diseases within regions of interest on CXR images and to improve screening accuracy. For digital image processes, a two-dimensional (2D) fractional-order convolution is used to enhance symptomatic features. The IFS with nonlinear interpolation functions is then used to reconstruct the 2D feature patterns. These reconstructed patterns are self-affine in the same class and thus help distinguish normal subjects from those with lung diseases. The accuracy rate is thus improved. Pooling is performed to reduce the dimensions of the feature patterns and speed up complex computations. A gray relational analysis-based classifier is used to identify the possible classes of the signs and symptoms of lung diseases. For digital CXR images in A-P view, the proposed multilayer machine learning classifier with k-fold cross-validation presents promising results in screening lung diseases and improving screening accuracy rate relative to traditional methods. The proposed classifier is evaluated in terms of recall (99.6%), precision (87.78%), accuracy (88.88%), and F1 score (0.9334).

中文翻译：

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使用迭代函数系统和多层分数阶机器学习分类器增强胸部 X 射线图像以提高筛选准确率

胸部 X 射线 (CXR) 图像通常用于识别患者症状的原因，包括肺部或心脏疾病的类别。在可视化检查中，前后 (A-P) 视图中的 CXR 成像是临床医生或放射科医生用来诊断可能的肺部异常的初步筛查方法，例如气胸 (Pt)、肺气肿 (E)、浸润 (In)、肺癌症 (M)、肺炎 (P)、肺纤维化 (F) 和胸腔积液 (Ef)。然而，确定与共存疾病相关的多种异常的原因是一个挑战。在排除疑似肺部疾病时，需要确定身体状况的体征和症状，才能做出明确的诊断。此外，低对比度 CXR 图像和手动检查限制了自动筛选应用。因此，本研究旨在提出迭代函数系统 (IFS) 和多层分数阶机器学习分类器，以快速筛选 CXR 图像上感兴趣区域内可能的肺部疾病类别，并提高筛选精度。对于数字图像处理，二维 (2D) 分数阶卷积用于增强症状特征。然后使用具有非线性插值函数的 IFS 来重建 2D 特征模式。这些重建的模式在同一类中是自仿射的，因此有助于区分正常受试者和患有肺部疾病的受试者。因此提高了准确率。执行池化以减少特征模式的维度并加速复杂计算。基于灰色关联分析的分类器用于识别肺部疾病的体征和症状的可能类别。对于 AP 视图中的数字 CXR 图像，所提出的具有 k 折交叉验证的多层机器学习分类器在筛查肺部疾病和提高相对于传统方法的筛查准确率方面取得了可喜的成果。所提出的分类器根据召回率 (99.6%)、精度 (87.78%)、准确率 (88.88%) 和 F1 分数 (0.9334) 进行评估。