title: Three-Class Mammogram Classification Based on Descriptive CNN Features.
abstract: 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.
authors.affiliation: Queen Mary University of London, London, UK; Faculty of Engineering and Technology, International Islamic University Islamabad, Islamabad, Pakistan.
authors.forename: M Mohsin
authors.identifier: 0000-0002-8242-4581
authors.initials: MM
authors.lastname: Jadoon
authors.affiliation: Queen Mary University of London, London, UK.
authors.forename: Qianni
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Faculty of Engineering and Technology, International Islamic University Islamabad, Islamabad, Pakistan.
authors.forename: Ihsan Ul
authors.identifier: 0000-0002-5692-1250
authors.initials: IU
authors.lastname: Haq
authors.affiliation: Faculty of Engineering and Technology, International Islamic University Islamabad, Islamabad, Pakistan.
authors.forename: Sharjeel
authors.identifier: 0000-0002-3396-8224
authors.initials: S
authors.lastname: Butt
authors.affiliation: Faculty of Engineering and Technology, International Islamic University Islamabad, Islamabad, Pakistan.
authors.forename: Adeel
authors.initials: A
authors.lastname: Jadoon
mesh_terms: D003198:Computer Simulation; D019992:Databases as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008327:Mammography / Q000379:methods*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D060388:Support Vector Machine; D058067:Wavelet Analysis