A package-SFERCB-"Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors"
A package-SFERCB-"Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors"
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DOI：
10.1016/j.asoc.2016.05.020
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发表时间：
2016-10-01
影响因子：
8.7
通讯作者：
Ahuja, Chirag Kamal
中科院分区：
文献类型：
--
作者：
Sachdeva, Jainy;Kumar, Vinod;Ahuja, Chirag Kamal
本实验的目的是开发一个交互式的CAD系统，以协助放射科医生在多类脑肿瘤分类。该研究是在55名患者的428张对比后T1加权MR图像的多样化数据集和10名患者的260张对比后T1加权MR图像的临床可用数据集上进行的。第一个数据集包括原发性脑肿瘤，如星形细胞瘤（AS）、多形性胶质母细胞瘤（GBM）、儿童肿瘤-成神经管细胞瘤（MED）和脑膜瘤（MEN），沿着继发性肿瘤-转移（MET）。第二个数据集包括星形细胞瘤（AS）、低级别胶质瘤（LGL）和脑膜瘤（MEN）。采用基于内容的活动轮廓模型（CBAC）对肿瘤区域进行标记。然后将区域保存为分割的感兴趣区域（SROI）。从这些SROI中提取71个强度和纹理特征集。这些特征是根据放射科医生提供的脑肿瘤的病理细节专门选择的。遗传算法（GA）从该输入集中选择最优特征集。两个混合机器学习模型的实现，使用遗传算法与支持向量机（SVM）和人工神经网络（ANN）（GA-SVM和GA-ANN），并在两个不同的数据集上进行了测试。GA-SVM用于寻找识别肿瘤类别的初步概率，GA-ANN用于确认准确性。第一个数据集的测试结果表明，遗传算法优化技术提高了支持向量机的整体准确率从79.3%到91.7%，神经网络从75.6%到94.9%。GA-SVM提供的单个类别准确率为：AS-89.8%，GBM-83.3%，MED-95.6%，MEN-91.8%和MET-97.1%。GA-ANN分类器的分类准确率分别为：AS-96.6%，GBM-86.6%，MED-93.3%，MEN-96%，MET-100%。对于第二个数据集也得到了类似的结果。支持向量机的总体准确率从80.8%提高到89%，人工神经网络的总体准确率从77.5%提高到94.1%。GA-SVM的分类准确率分别为：AS-85.3%，LGL-88.8%，MEN-93%。GA-ANN分类器的分类准确率分别为：AS-92.6%，LGL-94.4%，MED-95.3%。从实验中可以看出，GA-ANN分类器比GA-SVM分类器具有更好的分类效果。此外，可以观察到，沿着提供更精细的结果，GA-SVM在速度上提供优势，而GA-ANN在精度上提供优势。两个分类器的组合结果将有利于放射科医生形成一个更好的决策分类脑肿瘤。(C)© 2016 Elsevier B. V.版权所有。
The objective of this experimentation is to develop an interactive CAD system for assisting radiologists in multiclass brain tumor classification. The study is performed on a diversified dataset of 428 post contrast T1-weighted MR images of 55 patients and publically available dataset of 260 post contrast T1-weighted MR images of 10 patients. The first dataset includes primary brain tumors such as Astrocytoma (AS), Glioblastoma Multiforme (GBM), childhood tumor-Medulloblastoma (MED) and Meningioma (MEN), along with secondary tumor-Metastatic (MET). The second dataset consists of Astrocytoma (AS), Low Grade Glioma (LGL) and Meningioma (MEN). The tumor regions are marked by content based active contour (CBAC) model. The regions are than saved as segmented regions of interest (SROIs). 71 intensity and texture feature set is extracted from these SROIs. The features are specifically selected based on the pathological details of brain tumors provided by the radiologist. Genetic Algorithm (GA) selects the set of optimal features from this input set. Two hybrid machine learning models are implemented using GA with support vector machine (SVM) and artificial neural network (ANN) (GA-SVM and GA-ANN) and are tested on two different datasets. GA-SVM is proposed for finding preliminary probability in identifying tumor class and GA-ANN is used for confirmation of accuracy. Test results of the first dataset show that the GA optimization technique has enhanced the overall accuracy of SVM from 79.3% to 91.7% and of ANN from 75.6% to 94.9%. Individual class accuracies delivered by GA-SVM are: AS-89.8%, GBM-83.3%, MED-95.6%, MEN-91.8%, and MET-97.1%. Individual class accuracies delivered by GA-ANN classifier are: AS-96.6%, GBM-86.6%, MED-93.3%, MEN-96%, MET-100%. Similar results are obtained for the second dataset. The overall accuracy of SVM has increased from 80.8% to 89% and that of ANN has increased from 77.5% to 94.1%. Individual class accuracies delivered by GA-SVM are: AS-85.3%, LGL-88.8%, MEN-93%. Individual class accuracies delivered by GA-ANN classifier are: AS-92.6%, LGL-94.4%, MED-95.3%. It is observed from the experiments that GA-ANN classifier has provided better results than GA-SVM. Further, it is observed that along with providing finer results, GA-SVM provides advantage in speed whereas GA-ANN provides advantage in accuracy. The combined results from both the classifiers will benefit the radiologists in forming a better decision for classifying brain tumors. (C) 2016 Elsevier B.V. All rights reserved.