CN111553441A - 一种基于方差过滤算法的胃肠医学影像多分类方法 - Google Patents
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Abstract
本发明揭示了一种基于方差过滤算法的胃肠医学影像多分类方法,其包括以下步骤:1)首先对输入的胃镜图片及肠镜图片进行特征提取;2)对提取出的特征进行归一化、降维处理;3)将特征进行两两融合,得到六组新的特征;4)采用LinearSVC多分类算法对每组特征进行10折交叉验证,得到最大的准确率及对应的每个特征的4个权重;5)将每个特征的权重放在一起计算方差,然后按照方差大小对每个特征进行排序;6)将排序后的特征进行IFS‑LinearSVC多分类,获得准确率acc以及所使用的特征;7)选择准确率较高的两种特征融合方法作为分类模型,对胃肠镜医学影像进行多分类。本发明可更准确地进行影像学评估,可重复性高,能显著提高工作效率,减少误诊和漏诊。
Description
技术领域
本发明属于医学影像分类技术领域,特别是涉及一种基于方差过滤算法的胃肠医学影像多分类方法。
背景技术
人工智能是目前世界上处理医学影像的关键技术之一。虽然内镜检查方案降低了胃肠道恶性肿瘤的死亡率,但它们仍然是世界范围内死亡的主要原因,仍然是全球经济负担。提高胃肠道肿瘤的检出率,优化治疗策略,高质量的内镜检查,以识别胃肠道肿瘤和分类良性和恶性病变之间的关系对于胃肠医生来说是必不可少的。尤其是缺乏经验的内镜医师可能会错过胃肠病变,这可能会给患者带来严重的后果。人工智能技术可以辅助医生检测内窥镜图像的细微变化,可以促进消化道疾病的有效和准确诊断,从而有效避免对病灶的遗漏,如何在保证较高敏感性的同时有效降低检测的假阳性率,是AI未来研究的重点之一。
在大数据时代,大量数字图像和医疗记录的积累促使人们需要利用人工智能有效地处理这些数据,而这些数据已经成为机器自我学习的基本资源。胃肠道疾病已经成为中国最常见的病例之一,在中国新发病例和死亡病例中仅次于肺癌。内镜成像是一种有效且广泛使用的临床技术,用于检测和诊断胃肠道中的病变部位。如何准确地、高效地从内镜影像中辨别出病变部位和性质是胃肠道医生在提出诊断方案的前首要任务。
发明内容
本发明的主要目的在于提供一种基于方差过滤算法的胃肠医学影像多分类方法,可更准确地进行影像学评估,且可重复性高,能显著提高工作效率,减少误诊和漏诊,并可对疗效进行检测。
本发明通过如下技术方案实现上述目的:一种基于方差过滤算法的胃肠医学影像多分类方法,其包括以下步骤:
1)首先采用DAISY、HOG、LBP、STRUCTURE四种算法对输入的胃镜图片及肠镜图片进行特征提取,获得四组特征数据;
2)对提取出的四组特征数据进行归一化、PCA降维处理得到归一化特征数据A1、A2、A3、A4;
3)将归一化特征数据进行两两融合,得到六组新的特征B1、B2、B3、B4、B5、B6,并针对每组新的特征进行步骤4)-步骤5)处理;
其中,B1=(A1、A2);B2=(A1、A3);B3=(A1、A4);B4=(A2、A3);B5=(A2、A4);B6=(A3、A4);
6)将步骤5)获得的排序后的特征集合进行IFS-LinearSVC多分类,获得准确率acc以及所使用的特征;
7)根据步骤6)中所得的准确率acc数据,选择其中准确率最高和第二高的两种特征融合方法作为分类模型,并根据步骤6)中所获得的对应模型所使用的特征来进行特征提取,对胃肠镜医学影像进行多分类。
进一步的,所述步骤4)中,权重计算公式如下:
准确率acc的计算公式为:
其中,
TP:预测为正向(P),实际上预测正确(T),即判断为正向的正确率;
TN:预测为负向(N),实际上预测正确(T),即判断为负向的正确率;
FP:预测为正向(P),实际上预测错误(F),误报率;
FN:预测为负向(N),实际上预测错误(F),漏报率;
所述步骤4)得到的权重矩阵为:
与现有技术相比,本发明一种基于方差过滤算法的胃肠医学影像多分类方法的有益效果在于:通过方差过滤算法及特征筛选算法,提高胃肠医学影像病变性质分类的准确率,可作为辅助医师的工具无缝集成到临床工作流程中,可以通过提供预先筛选的图像和确定的特征可更准确地进行影像学评估,且可重复性高,能显著提高工作效率,减少误诊和漏诊,并可对疗效进行检测。
【附图说明】
图1为本发明实施例的流程原理示意图。
【具体实施方式】
实施例:
请参照图1,本实施例一种基于方差过滤算法的胃肠医学影像多分类方法,其包括以下步骤:
1)首先采用DAISY[1]([1]Tola, Engin, Vincent Lepetit, and Pascal Fua.“DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo.” IEEETransactions on Pattern Analysis and Machine Intelligence. 2010;32(5):815–30.)、HOG[2]([2]Dalal, Navneet , and B. Triggs. "Histograms of OrientedGradients for Human Detection." IEEE Computer Society Conference on ComputerVision & Pattern Recognition IEEE, 2005.)、LBP[3]([3] Ojala, T., Pietikäinen,M. and Harwood, D. (1994) Performance evaluation of texture measures withclassification based on Kullback discrimination of distributions. Proceedingsof the 12th IAPR International Conference on Pattern Recognition (ICPR 1994),1, pp. 582-585.)、STRUCTURE[4]([4] V. B. S. Prasath, R. Pelapur, G. Seetharamanand K. Palaniappan, "Multiscale Structure Tensor for Improved FeatureExtraction and Image Regularization," in IEEE Transactions on ImageProcessing, 2019;28(12): 6198-6210.)四种算法对输入的胃镜图片及肠镜图片进行特征提取,获得四组特征数据;
其中,胃镜图片分为健康的胃、胃炎、胃息肉、胃溃疡、胃癌五类;肠镜图片分为健康的肠道、肠炎、肠息肉、肠癌四类;
2)对提取出的四组特征数据进行归一化、PCA降维处理得到归一化特征数据A1、A2、A3、A4;
3)将归一化特征数据进行两两融合,得到六组新的特征B1、B2、B3、B4、B5、B6,并针对每组新的特征进行步骤4)-步骤5)处理;
其中,B1=(A1、A2);B2=(A1、A3);B3=(A1、A4);B4=(A2、A3);B5=(A2、A4);B6=(A3、A4);
准确率acc的计算公式为:
TP:预测为正向(P),实际上预测正确(T),即判断为正向的正确率;
TN:预测为负向(N),实际上预测正确(T),即判断为负向的正确率;
FP:预测为正向(P),实际上预测错误(F),误报率;
FN:预测为负向(N),实际上预测错误(F),漏报率。
得到权重矩阵如下:
6)将步骤5)获得的排序后的特征集合进行IFS-LinearSVC多分类,获得准确率acc以及所使用的特征;
7)根据步骤6)中所得的准确率acc数据,选择其中准确率最高和第二高的两种特征融合方法作为分类模型,并根据步骤6)中所获得的对应模型所使用的特征来进行特征提取,对胃肠镜医学影像进行多分类。
本实施例中,利用针对胃肠图片进行了训练及测试,其中HOG+STRUCTURE以及DAISY+LBP的融合效果较好,在经过特征权重方差排序后,保留HOG+STRUCTURE中的前70~75个特征、DAISY+LBP中的前340~350个特征分类效果较高,故采用HOG+STRUCTURE和DAISY+LBP训练得到的模型用于将来的胃肠镜医学影像的多分类。
以上所述的仅是本发明的一些实施方式。对于本领域的普通技术人员来说,在不脱离本发明创造构思的前提下,还可以做出若干变形和改进,这些都属于本发明的保护范围。
Claims (2)
1.一种基于方差过滤算法的胃肠医学影像多分类方法,其特征在于:其包括以下步骤:
1)首先采用DAISY、HOG、LBP、STRUCTURE四种算法对输入的胃镜图片及肠镜图片进行特征提取,获得四组特征数据;
2)对提取出的四组特征数据进行归一化、PCA降维处理得到归一化特征数据A1、A2、A3、A4;
3)将归一化特征数据进行两两融合,得到六组新的特征B1、B2、B3、B4、B5、B6,并针对每组新的特征进行步骤4)-步骤5)处理;
其中,B1=(A1、A2);B2=(A1、A3);B3=(A1、A4);B4=(A2、A3);B5=(A2、A4);B6=(A3、A4);
6)将步骤5)获得的排序后的特征集合进行IFS-LinearSVC多分类,获得准确率acc以及所使用的特征;
7)根据步骤6)中所得的准确率acc数据,选择其中准确率最高和第二高的两种特征融合方法作为分类模型,并根据步骤6)中所获得的对应模型所使用的特征来进行特征提取,对胃肠镜医学影像进行多分类。
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