具有双重分形特征的指数像素化积分变换用于增强胸部X射线异常检测
Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection
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中文总结 AI 辅助
针对胸部X射线低对比度等检测难题，提出指数像素化积分变换结合双重分形特征与多元自适应回归样条分类，在基准数据集上实现高准确率（98.46%-99.45%）和F1分数（96.53%-98.10%）。
中文摘要 AI 辅助
呼吸系统疾病的高发病率，尤其是因新型冠状病毒导致死亡人数显著上升而加剧的情况，凸显了早期检测和及时干预的关键需求。这一需求至关重要，具有深刻影响并挽救众多生命的潜力。在医学上，胸部X射线摄影是诊断和评估多种呼吸系统疾病严重程度的一种重要且经济可行的医学成像方法。然而，由于低对比度问题、组织结构重叠、主观变异性和噪声的存在，即使在训练有素的放射科医生看来，在胸部X射线中检测这些疾病也是一项繁琐的任务。为了解决这些问题，本研究引入了一种名为指数像素化积分的新型分析模型，用于自动检测胸部X射线中的感染。首先，所提出的指数像素化积分增强像素强度以克服低对比度问题，然后进行极坐标变换，随后使用局部不变的曼德博和朱莉娅分形几何进行表示，以有效区分结构特征。将收集到的特征（称为具有双重分形特征的指数像素化积分）通过非参数多元自适应回归样条进行分类，以在每对类别之间建立集成模型，从而有效诊断多种疾病。对所提出的分类框架在大型医学基准数据集上的严格分析展示了其相对于同类方法的优越性，其分类准确率和F1分数分别高达98.46%至99.45%和96.53%至98.10%，使其成为诊断呼吸系统疾病的精确且可解释的自动化系统。
英文摘要
The heightened prevalence of respiratory disorders, particularly exacerbated by a significant upswing in fatalities due to the novel coronavirus, underscores the critical need for early detection and timely intervention. This imperative is paramount, possessing the potential to profoundly impact and safeguard numerous lives. Medically, chest radiography stands out as an essential and economically viable medical imaging approach for diagnosing and assessing the severity of diverse Respiratory Disorders. However, their detection in Chest X-Rays is a cumbersome task even for well-trained radiologists owing to low contrast issues, overlapping of the tissue structures, subjective variability, and the presence of noise. To address these issues, a novel analytical model termed Exponential Pixelating Integral is introduced for the automatic detection of infections in Chest X-Rays in this work. Initially, the presented Exponential Pixelating Integral enhances the pixel intensities to overcome the low-contrast issues that are then polar-transformed followed by their representation using the locally invariant Mandelbrot and Julia fractal geometries for effective distinction of structural features. The collated features labeled Exponential Pixelating Integral with dually characterized fractal features are then classified by the non-parametric multivariate adaptive regression splines to establish an ensemble model between each pair of classes for effective diagnosis of diverse diseases. Rigorous analysis of the proposed classification framework on large medical benchmarked datasets showcases its superiority over its peers by registering a higher classification accuracy and F1 scores ranging from 98.46 to 99.45% and 96.53-98.10% respectively, making it a precise and interpretable automated system for diagnosing respiratory disorders.
发表机构
- The Construct（建构研究院）
- Involgixs Inc（Involgixs公司）
- Vellore Institute of Technology（韦洛尔理工学院）
机构由 AI 辅助整理，请以论文原文为准。
