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Computer-Aided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms.
Computers in Biology and Medicine ( IF 7.7 ) Pub Date : 2020-07-31 , DOI: 10.1016/j.compbiomed.2020.103940
Anisha Isaac 1 , H Khanna Nehemiah 1 , Anubha Isaac 2 , A Kannan 3
Affiliation  

Pulmonary emphysema is a condition characterized by the destruction and permanent enlargement of the alveoli of the lungs. The destruction of gas-exchanging alveoli causes shortness of breath followed by a chronic cough and sputum production. A Computer-Aided Diagnosis (CAD) framework for diagnosing pulmonary emphysema from chest Computed Tomography (CT) slices has been designed and implemented in this study. The process of implementing the CAD framework includes segmenting the lung tissues and extracting the regions of interest (ROIs) using the Spatial Intuitionistic Fuzzy C-Means clustering algorithm. The ROIs that were considered in this work were emphysematous lesions — namely, centrilobular, paraseptal, and bullae that were labelled by an expert radiologist. The shape, texture, and run-length features were extracted from each ROI. A wrapper approach that employed four bio-inspired algorithms — namely, Moth–Flame Optimization (MFO), Firefly Optimization (FFO), Artificial Bee Colony Optimization, and Ant Colony Optimization — with the accuracy of the support vector machine classifier as the fitness function was used to select the optimal feature subset. The selected features of each bio-inspired algorithm were trained independently using the Extreme Learning Machine classifier based on the tenfold cross-validation technique. The framework was tested on real-time and public emphysema datasets to perform binary classification of lung CT slices of patients with and without the presence of emphysema. The framework that used MFO and FFO for feature selection produced superior results regarding accuracy, precision, recall, and specificity for the real-time dataset and the public dataset, respectively, when compared to the other bio-inspired algorithms.



中文翻译:

使用生物启发算法的计算机辅助诊断系统,用于诊断肺气肿。

肺气肿是一种以肺泡破坏和永久性肿大为特征的疾病。交换气体的肺泡的破坏导致呼吸急促,继而引起慢性咳嗽和痰液的产生。本研究设计并实施了一种计算机辅助诊断(CAD)框架,用于从胸部计算机断层扫描(CT)切片诊断肺气肿。实施CAD框架的过程包括使用空间直觉模糊C均值聚类算法对肺组织进行分割并提取感兴趣区域(ROI)。在这项工作中考虑的ROI是气肿性病变-即小叶,副隔和大疱,由放射线专家标记。从每个ROI中提取形状,纹理和游程特征。一种采用四种生物启发算法的包装方法,即飞蛾-火焰优化(MFO),萤火虫优化(FFO),人工蜂群优化和蚁群优化-支持向量机分类器的精度作为适应度函数用于选择最佳特征子集。使用基于十倍交叉验证技术的极限学习机分类器,对每种生物启发算法的选定功能进行了独立训练。该框架在实时和公共肺气肿数据集上进行了测试,以对有或没有肺气肿的患者的肺部CT切片进行二分类。使用MFO和FFO进行功能选择的框架在准确性,精度,召回率,

更新日期:2020-07-31
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