Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2018 (2018), Article ID 2362108, 20 pages
Research Article

Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering

School of Engineering, Macquarie University, Sydney, NSW 2109, Australia

Correspondence should be addressed to Abdullah-Al Nahid

Received 30 October 2017; Revised 25 January 2018; Accepted 6 February 2018; Published 7 March 2018

Academic Editor: Wen-Hwa Lee

Copyright © 2018 Abdullah-Al Nahid et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 200 dataset, the best Precision value 96.00% is achieved on the 40 dataset, and the best F-Measure value is achieved on both the 40 and 100 datasets.