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乳腺癌是现代女性最常见的恶性肿瘤之一。支持向量机SVM是一种基于统计学习理论的机器学习算法,它能在训练样本很少的情况下达到良好的分类效果。本文提出一个基于支持向量机的超声乳腺肿瘤图像计算机辅助诊断系统,它由图像预处理、ROI特征提取和SVM分类器异常诊断三个模块构成。通过实验证明,在处理相同的样本数据集时,基于SVM算法的计算机辅助诊断系统相对于BP神经网络,有更高的诊断灵敏度。统计学习理论的发展将更加完善SVM,具有高分类性能的分类器将使计算机辅助诊断的能力进一步提高。 相似文献
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INTRODUCTION Support Vector Machine (SVM) is a relativelynew soft computing method based on statisticallearning theory presented by Vapnik (1995). In SVM,original input space is mapped into a high dimen-sional dot product space called feature space in whichthe optimal hyperplane is determined to maximize thegeneralization ability of the classifier. The optimalhyperplane is found by exploiting a branch ofmathematics, called optimization theory, and re-specting the insights provided by … 相似文献
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乳腺癌严重威胁女性健康和生命,及时诊断并提供治疗方案给医生带来了挑战,病理图像分类结果是医生确诊的重要依据,实现乳腺癌病理图像识别分类具有重要意义及临床应用价值。近年来,大多数研究集中于良恶性分类,而不同类型的乳腺肿瘤本身具有不同病因及治疗方法。采用 Inception-ResNet-V2 深度卷积神经网络模型,实现对乳腺癌病理图像的八分类,利用数据增强和迁移学习方法,在 Matlab 上对数据集 BreaKHis进行实验。结果表明,该方法识别率基本达到 80%以上,比大部分已有研究成果效果更优。 相似文献
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Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm 总被引：2，自引：0，他引：2
INTRODUCTION Recent techniques based on oligonucleotide or cDNA microarrays allow the expression level of thousands of genes to be monitored in parallel (Golub et al., 1999). A critically important factor for cancer diagnosis and treatment is the reliable prediction of tumor progression. A remarkable advance for mo- lecular biology and for cancer research is cDNA mi- croarray technology. cDNA microarray datasets havea high dimensionality corresponding to the large number of genes monit… 相似文献
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目的：研究乳腺肿瘤组织印片细胞学检查与组织病理学定性诊断符合率,探讨印片细胞学检查在临床病理学诊断中的应用价值。方法：回顾性分析2004-01—2008-01月采用印片法观察123例新鲜离体乳腺包块,与组织病理切片诊断结果对照分析。结果：总体定性诊断符合率95．93％（118／123）,其中恶性肿瘤96．3％,良性肿瘤为95．2％,误诊率为2．44％。结论：组织印片细胞学检查是组织病理学诊断一种有效的补充,也是对术中切除组织筛检定性的一种快捷方法。 相似文献
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目的：寻求胰腺癌的诊断方法，方法：用免疫放射分析法（IRMA）检测血清CA－19－9含量，106例血清CA－19－9含量3700u/L，进行追踪随访，结果：106例中，胰腺癌66例，结肠癌15例，胆管癌13例，肺癌9例，结论：检测血清CA－19－9含量，有利于胰腺癌的诊断和与结肠癌，胆管癌，肺癌等恶性肿瘤的鉴别。 相似文献
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乳腺内钙化在良恶性病变中X线诊断价值 总被引：1，自引：0，他引：1
目的:探讨乳腺内钙化在乳腺良恶性病变中线的X诊断价值。方法:经病理证实的,乳腺X线摄影确认的62例钙化病例,回顾性分析其钙化的形态、大小、密度、边缘、分布等特点。结果:62例乳腺钙化病例中,良性病变为43例,恶性病变19例。结论:乳腺内钙化的形态有助于乳腺良恶性病变的鉴别诊断。 相似文献
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针对实际电能质量扰动种类繁多、扰动信号差异不明显、存在多种混合扰动,导致识别电能质量非常困难的情况,提出一种基于极点对称经验模式分解方法（ESMD）和支持向量机（SVM）的电能质量混合扰动信号分类识别新方法。首先,对加入白噪声的混合扰动信号利用小波软阈值去噪处理|其次,利用ESMD将信号分解为不同信号分量,对每类扰动的不同信号分量分别提取样本熵和互样本熵特征值,所有分量特征值构成特征向量|最后利用SVM对扰动信号特征向量进行分类和混合扰动识别。研究表明,该方法对混合扰动识别正确率很高,是一个有效的方法。 相似文献
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目的 :明确良恶性乳腺肿块在超声中的鉴别征象 ;评价超声结合钼靶摄影后在乳腺癌诊断中的价值。方法 :报告乳腺肿块病例 72例 ,包括乳腺癌 4 0例 ,良性病变 32例 ,均行超声检查 ,其中 6 0例行钼靶摄影 .。结果 :(1)乳腺癌的超声表现 :不规则形或圆形 ,纵横比 >1,边缘毛刺 ,边缘成角 ,后方声影 ,微小钙化 ,较丰富血流、不规则血流。 (2 )超声与钼靶摄影的结果相比 ,在良恶性乳腺疾病的鉴别方面有相同价值 ,而超声更具实用性 ,在定位与转移淋巴结检出方面优于钼靶摄影。而钼靶摄影在微小钙化的检出方面优于超声。结论 :超声和钼靶摄影二者结合在发现乳腺肿块及其良恶性鉴别诊断方面有很大价值。 相似文献
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应用免疫组化SP法对94例乳腺癌、41例乳腺良性病变和10例正常乳腺组织进行了细胞粘附分子CD_(15)检测。结果发现,CD_(15)。在乳腺癌和乳腺良性病变的阳性率分别为79.8％和58.3％,两者有显著差异性(P<0 01),10例正常乳腺组织仅4例呈CD_(15)弱阳性反应。CD_(15)表达与患者年龄和肿瘤大小无显著关系。CD_(15)表达阳性率在浸润性导管癌中显著高于单纯癌(P<0.05),组织学Ⅱ～Ⅲ级显著高于Ⅰ级者(P<0.05),临床Ⅲ一Ⅳ期显著高于Ⅰ期和Ⅱ期者(P<0.05),淋巴结转移阳性组显著高于阴性组(P<0.01)。在一组原发部位和淋巴结转移性乳腺癌配对标本中,CD_(15)表达无明显差异性。CD_(15)阳性的乳腺癌Cath-D和c-erbB-2的表达阳性率均显著高于CD_(15)阴性者(P<0.001)。结果提示,CD_(15)的表达与乳腺癌的发生发展、浸润转移及预后有密切关系。 相似文献
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支持向量机(Support Vector Machine,SVM)在解决小样本、非线性及高维模式识别中具有优势,但核函数的选取没有定论,且其参数对SVM模型的性能起重要作用。针对这些问题,文章建立了基于SVM的分类模型,并通过UCI数据集验证了径向基核函数(Radial Basis Function,RBF)较其他核函数的有效性,其中核参数的选取采用改进的网格搜索法进行寻优。分类实验结果表明,选择RBF核函数的分类准确度较其他核函数提高了2.5%到35%。 相似文献
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张红梅 《扬州职业大学学报》2007,11(2):31-34
在统计学习理论框架下产生的支持向量机这一新的通用机器学习方法,能较好地解决小样本、非线性、高维数和局部极小点等实际问题,已成为机器学习界的研究热点之一.文章归纳了支持向量机在电力系统故障诊断、暂稳分类、负荷预测、谐波分析等方面的应用现状,并提出了可能进一步应用的方面. 相似文献
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针对滚动轴承故障分类准确率低的问题,提出一种利用遗传算法结合粒子群算法优化支持向量机分类器的故障诊断方法.实验通过提取滚动轴承不同故障状态下的振动信号,以转化成时域和频域组成的特征集为特征向量,利用粒子群生成二维粒子,即惩罚因子C、核函数参数G,并喂入支持向量机进行训练和交叉验证,取最优适应度对应的粒子,进而构建遗传粒... 相似文献
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Diabetic retinopathy (DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates (EXs), in retinal images can contribute to the diagnosis and screening of the disease. To achieve this goal, an automatically detecting approach based on improved FCM (IFCM) as well as support vector machines (SVM) was established and studied. Firstly, color fundus images were segmented by IFCM, and candidate regions of EXs were obtained. Then, the SVM classifier is confirmed with the optimal subset of features and judgments of these candidate regions, as a result hard exudates are detected from fundus images. Our database was composed of 126 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 56 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 94.65 and a mean positive predictive value of 97.25 . With an image-based criterion, our approach reached a 100 mean sensitivity, 96.43 mean specificity and 98.21 mean accuracy. Furthermore, the average time cost in processing an image is 4.56 s. The results suggest that the proposed method can efficiently detect EXs from color fundus images and it could be a diagnostic aid for ophthalmologists in the screening for DR. 相似文献
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Motivation: It was found that high accuracy splicing-site recognition of rice (Oryza sativa L.) DNA sequence is especially difficult. We described a new method for the splicing-site recognition of rice DNA sequences. Method: Based on the intron in eukaryotic organisms conforming to the principle of GT-AG, we used support vector machines (SVM) to predict the splicing sites. By machine learning, we built a model and used it to test the effect of the test data set of true and pseudo splicing sites. Results: The prediction accuracy we obtained was 87.53% at the true 5' end splicing site and 87.37% at the true 3' end splicing sites. The results suggested that the SVM approach could achieve higher accuracy than the previous approaches. 相似文献
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ZHANG Ri-dong WANG Shu-qing 《浙江大学学报(A卷英文版)》2006,7(12):1984-1988
INTRODUCTION Bilinear systems are a kind of important nonlinear systems with relatively simple structure, and many industrial processes can be described as a bilinear system. Thus research on the control of this kind of systems is very important. On the other hand, model predictive control (MPC) (Clarke et al., 1987) has been widely used in industrial applications and many predictive control methods focusing on bilinear systems are emerging (Bloemen et al., 2001; Fontes et al., 2004; He… 相似文献
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如何从小样本、高维度特性的功能磁共振成像（fMRI）数据中识别出内在的脑区活动模式,对理解人脑意义重大。随着模式识别技术和机器学习算法的发展,fMRI的分类研究也引起了人们的重视。提出一种对fMRI数据分类的加权随机SVM集群（WRSVMC）算法。该算法分为两步,首先通过随机选择样本和特征建立多个SVM,以构建集成分类器;然后在投票过程中,对每个SVM赋权重,以优化模型的集成性能。结合fMRI数据和图论特征,采用WRSVMC算法对轻度认知障碍（MCI）患者数据展开分类研究。结果表明,准确率最高可达87.67%。该方法能帮助医师对MCI患者进行辅助诊断。 相似文献
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The behavior of schools of zebrafish (Danio rerio) was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment
using support vector machine (SVM) was developed. The behavioral parameters of fish were recorded and analyzed during one
hour in an environment of a 24-h half-lethal concentration (LC50) of a pollutant. The data were used to develop a method to evaluate water quality, so as to give an early indication of toxicity.
Four kinds of metal ions (Cu2+, Hg2+, Cr6+, and Cd2+) were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic
algorithm (GA) was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost
was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective
approach to the classification of water quality. 相似文献
19.
统计学习与支持向量机 总被引：1，自引：0，他引：1
牟廉明 《内江师范学院学报》2002,17(6):3-7
支持向量机（SVM）是一类新型机器学习方法，其理论基础是统计学习理论，由于其出色的学习性能而成为当前国际机器学习领域的研究热点。该首先阐述统计学习的核心内容，然后对SVM及其应用进行研究，最后讨论了SVM的局限和有等研究的问题。 相似文献
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INTRODUCTIONCorrectlypinpointingsplicing sitesingenomicDNAsequencesisnotaneasytask ,whichisofgreatimportancetothegenomeannotationandgenefinding .Intronsaregenerallydividedinto3classes,namelycalssI,classIIandcommonnu cleuspre mRNA .IntronofclassIandIIcango… 相似文献