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Information 2018, 9(1), 19; https://doi.org/10.3390/info9010019

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
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Received: 18 December 2017 / Revised: 7 January 2018 / Accepted: 12 January 2018 / Published: 16 January 2018
(This article belongs to the Special Issue Information-Centered Healthcare)
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Abstract

Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD) techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist’s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN) technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC) images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information—for example, the Local Binary Pattern (LBP) represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT) gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT) derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a) Convolutional Neural Network Raw Image (CNN-I); (b) Convolutional Neural Network CT Histogram (CNN-CH); (c) Convolutional Neural Network CT LBP (CNN-CL); (d) Convolutional Neural Network Discrete Fourier Transform (CNN-DF); (e) Convolutional Neural Network Discrete Cosine Transform (CNN-DC). We have performed our experiments on the BreakHis image dataset. The best performance is achieved when we utilize the CNN-CH model on a 200× dataset that provides Accuracy, Sensitivity, False Positive Rate, False Negative Rate, Recall Value, Precision and F-measure of 92.19%, 94.94%, 5.07%, 1.70%, 98.20%, 98.00% and 98.00%, respectively. View Full-Text
Keywords: classification; Convolutional Neural Network; Contourlet Transform; Histogram; Discrete Fourier Transform; Discrete Cosine Transform; Local Binary Pattern classification; Convolutional Neural Network; Contourlet Transform; Histogram; Discrete Fourier Transform; Discrete Cosine Transform; Local Binary Pattern
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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. (CC BY 4.0).
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Nahid, A.-A.; Kong, Y. Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network. Information 2018, 9, 19.

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