Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering.
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 nontrivi...
| Publicado en: | BioMed Research International Vol. 2018; pp. 1 - 21 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
3/7/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=128425754&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128425754 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/7/2018 vid: 2018 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 128425754 128425754 128425754 10.1155/2018/2362108 128425754 ppf: 1 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering. aug: au: Nahid, Abdullah-Al Mehrabi, Mohamad Ali Kong, Yinan affil: School of Engineering, Macquarie University, Sydney, NSW 2109, Australia sug: subj: Breast Neoplasms Pathology Breast Neoplasms Diagnosis Neural Networks (Computer) Image Processing, Computer Assisted Computer-Aided Design Decision Making, Clinical Cluster Analysis Memory, Short Term Validity ab: 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<italic> F</italic>-Measure value is achieved on both the 40x and 100x datasets. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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