Ramanujan Computing Centre, Anna University, Chennai, 600025 India.
Ramanujan Computing Centre, Anna University, Chennai, 600025 India.
Comput Methods Programs Biomed. 2017 Jul;145:115-125. doi: 10.1016/j.cmpb.2017.04.009. Epub 2017 Apr 18.
Computer-aided diagnosis (CAD) plays a vital role in the routine clinical activity for the detection of lung disorders using computed tomography (CT) images. It serves as a source of second opinion that radiologists may consider in order to interpret CT images. In this work, the purpose of CAD is to improve the diagnostic accuracy of pulmonary bronchitis from CT images of the lung.
Left and right lung fields are segmented using optimal thresholding from the lung CT images. Texture and shape features are extracted from the pathology bearing regions. A hybrid feature selection approach based on ant colony optimization (ACO) combining cosine similarity and support vector machine (SVM) classifier is used to select relevant features. Additionally, tandem run recruitment strategy is included in the selection activity to choose the promising features. The SVM classifier is trained using the selected features and the performance of the trained classifier is evaluated using trivial performance evaluation measures.
The training and testing datasets used in building the classifier model are disjoint and contains 200 CT slices affected with bronchitis, 50 normal slices and 300 slices with cancer. Out of 100 features extracted from each CT slice, a subset of 60 features is used for classification. ACO with tandem run strategy yielded 81.66% of accuracy whereas ACO without tandem run yielded an accuracy of 77.52%. When all the features are used for classifier training without feature selection algorithm, an accuracy of 75.14% is achieved.
From the results, it is inferred that identifying relevant features to train the classifier has a definite impact on the classifier performance.
计算机辅助诊断（CAD）在使用计算机断层扫描（CT）图像检测肺部疾病的常规临床活动中发挥着重要作用。它可以作为放射科医生考虑的第二个意见来源，以解释 CT 图像。在这项工作中，CAD 的目的是提高从肺部 CT 图像中检测肺部支气管炎的诊断准确性。
使用最优阈值从肺部 CT 图像中分割左右肺区。从病理承载区域中提取纹理和形状特征。采用基于蚁群优化（ACO）的混合特征选择方法，结合余弦相似度和支持向量机（SVM）分类器，选择相关特征。此外，选择活动中还包括串联运行招聘策略，以选择有前途的特征。使用选定的特征训练 SVM 分类器，并使用简单的性能评估措施评估训练后的分类器的性能。
用于构建分类器模型的训练和测试数据集是不相交的，包含 200 张受支气管炎影响的 CT 切片、50 张正常切片和 300 张癌症切片。从每个 CT 切片中提取的 100 个特征中，选择了 60 个特征子集用于分类。带有串联运行策略的 ACO 产生了 81.66%的准确率，而没有串联运行策略的 ACO 产生了 77.52%的准确率。当不使用特征选择算法对所有特征进行分类器训练时，准确率达到 75.14%。
从结果可以推断，识别相关特征来训练分类器对分类器性能有一定的影响。
