Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering
Joint Authors
Nahid, Abdullah-Al
Mehrabi, Mohamad Ali
Kong, Yinan
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-07
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Nahid, Abdullah-Al& Mehrabi, Mohamad Ali& Kong, Yinan. 2018. Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering. BioMed Research International،Vol. 2018, no. 2018, pp.1-20.
https://search.emarefa.net/detail/BIM-1124988
Modern Language Association (MLA)
Nahid, Abdullah-Al…[et al.]. Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering. BioMed Research International No. 2018 (2018), pp.1-20.
https://search.emarefa.net/detail/BIM-1124988
American Medical Association (AMA)
Nahid, Abdullah-Al& Mehrabi, Mohamad Ali& Kong, Yinan. Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-20.
https://search.emarefa.net/detail/BIM-1124988
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1124988