Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering.
Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering.
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DOI：
10.1155/2018/2362108
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发表时间：
2018
影响因子：
--
通讯作者：
Kong Y
中科院分区：
文献类型：
--
作者：
Nahid AA;Mehrabi MA;Kong Y
乳腺癌是一个严重的威胁，也是全世界妇女死亡的最大原因之一。癌症的识别在很大程度上依赖于医生和内科医生的数字生物医学摄影分析，例如组织病理学图像。分析组织病理学图像是一项重要的任务，从这些类型的图像的调查决策总是需要专业知识。然而，计算机辅助诊断（CAD）技术可以帮助医生做出更可靠的决定。最先进的深度神经网络（DNN）最近被引入用于生物医学图像分析。通常，每个图像包含结构和统计信息。本文分类一组生物医学乳腺癌图像（BreakHis数据集）使用新的DNN技术指导的结构和统计信息来自图像。具体而言，提出了卷积神经网络（CNN），长短期记忆（LSTM）以及CNN和LSTM的组合用于乳腺癌图像分类。Softmax和支持向量机（SVM）层已被用于决策阶段后，利用提出的新DNN模型提取特征。在本实验中，在200 x数据集上实现了91.00%的最佳准确度值，在40 x数据集上实现了96.00%的最佳精度值，并且在40 x和100 x数据集上都实现了最佳F-Measure值。
Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F-Measure value is achieved on both the 40x and 100x datasets.
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影响因子：
3.7
作者：
Araújo T;Aresta G;Castro E;Rouco J;Aguiar P;Eloy C;Polónia A;Campilho A
通讯作者：
Campilho A
影响因子：
1.9
作者：
FUKUSHIMA, K
通讯作者：
FUKUSHIMA, K
影响因子：
3.7
作者：
Dimitropoulos K;Barmpoutis P;Zioga C;Kamas A;Patsiaoura K;Grammalidis N
通讯作者：
Grammalidis N
影响因子：
6
作者：
Jiao, Zhicheng;Gao, Xinbo;Li, Jie
通讯作者：
Li, Jie
影响因子：
10.6
作者：
Sahiner, B;Chan, HP;Goodsitt, MM
通讯作者：
Goodsitt, MM