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Three-Class Mammogram Classification Based on Descriptive CNN Features
Joint Authors
Haq, Ihsanul
Jadoon, M. Mohsin
Zhang, Qianni
Butt, Sharjeel
Jadoon, Adeel
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-15
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed.
The proposed model targets a three-class classification study (normal, malignant, and benign cases).
In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT).
An augmented data set is generated by using mammogram patches.
To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE).
In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used.
In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted.
Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN).
Softmax layer and support vector machine (SVM) layer are used to train CNN for classification.
Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures.
CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively.
Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.
American Psychological Association (APA)
Jadoon, M. Mohsin& Zhang, Qianni& Haq, Ihsanul& Butt, Sharjeel& Jadoon, Adeel. 2017. Three-Class Mammogram Classification Based on Descriptive CNN Features. BioMed Research International،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1136179
Modern Language Association (MLA)
Jadoon, M. Mohsin…[et al.]. Three-Class Mammogram Classification Based on Descriptive CNN Features. BioMed Research International No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1136179
American Medical Association (AMA)
Jadoon, M. Mohsin& Zhang, Qianni& Haq, Ihsanul& Butt, Sharjeel& Jadoon, Adeel. Three-Class Mammogram Classification Based on Descriptive CNN Features. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1136179
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136179