Research Article Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering Abdullah-Al Nahid , Mohamad Ali Mehrabi, and Yinan Kong School of Engineering, Macquarie University, Sydney, NSW 2109, Australia Correspondence should be addressed to Abdullah-Al Nahid; abdullah-al.nahid@students.mq.edu.au 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. his 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. he identiication 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. he state- of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. his paper classiies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Speciically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classiication. Sotmax and Support Vector Machine (SVM) layers have been used for the decision-making stage ater 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. 1. Introduction he unwanted growth of cells causes cancer which is a serious threat to humans. Statistics show that millions of people all over the world sufer various cancer diseases. As an example Table 1 summarises the statistics concerning the recent cancer situation in Australia. hese statistics reveal the number of newly cancer-afected people diagnosed in Australia and also the number of people who died in 2017 in Australia. hese statistics also divulge that the number of females afected and the number of females dying due to breast cancer are more than the numbers for males. his indicates that females are more vulnerable to breast cancer (BC) than males. Although these statistics are for Australia they might be representative of what is happening throughout the world. Proper BC diagnosis can save thousands of women’s lives, and proper diagnosis largely depends on identiication of the cancer. Finding BC largely depends on capturing a photo- graph of the cancer-afected area which gives information about the current situation of the cancer. A few biomedical imaging techniques have been utilised, some of which are noninvasive such as Ultrasound imaging, X-ray imaging, and Computer Aided Tomography (CAT) imaging. Other imaging techniques are invasive such as histopathological images. Investigation of these kinds of images is always very challenging, especially in the case of histopathological imaging due to its complex nature. Histopathological image analysis is nontrivial, and the investigation of this kind of image always produces some contradictory decisions by doctors. Since doctors and physicians are human, it is natural that errors will occur. A Computer Aided Diagnosis (CAD) system provides doctors and physicians with valuable information, for exam- ple, classiication of the disease. Diferent research groups investigate opportunities to improve the CAD systems’ per- formance. Some advanced engineering techniques have been utilised to take a general image classiier and adjust it as a biomedical image classiier, such as a breast image classiier. Hindawi BioMed Research International Volume 2018, Article ID 2362108, 20 pages https://doi.org/10.1155/2018/2362108