Ramanujan Computing Centre, Anna University, Chennai, 600025, TN, India.
Ramanujan Computing Centre, Anna University, Chennai, 600025, TN, India.
Comput Biol Med. 2020 Sep;124:103940. doi: 10.1016/j.compbiomed.2020.103940. Epub 2020 Jul 31.
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.
肺气肿是一种以肺部肺泡破坏和永久性扩大为特征的疾病。气交换肺泡的破坏导致呼吸急促，随后出现慢性咳嗽和咳痰。本研究设计并实现了一种用于从胸部计算机断层扫描 (CT) 切片诊断肺气肿的计算机辅助诊断 (CAD) 框架。实现 CAD 框架的过程包括使用空间直觉模糊 C 均值聚类算法分割肺组织并提取感兴趣区域 (ROI)。本工作考虑的 ROI 是肺气肿病变——即由专家放射科医生标记的小叶中心性、间隔旁和大疱性肺气肿病变。从每个 ROI 提取形状、纹理和游程长度特征。采用四种仿生算法—— moth-flame 优化 (MFO)、萤火虫优化 (FFO)、人工蜂群优化和蚁群优化——的包装方法，以支持向量机分类器的准确性作为适应度函数，选择最优特征子集。基于十折交叉验证技术，使用极端学习机分类器对每个仿生算法选择的特征进行独立训练。该框架在实时和公共肺气肿数据集上进行测试，对有和没有肺气肿的患者的肺 CT 切片进行二进制分类。与其他仿生算法相比，使用 MFO 和 FFO 进行特征选择的框架在实时数据集和公共数据集上的准确性、精度、召回率和特异性方面分别产生了更好的结果。
