Detection of abnormalities in mammograms using deep features

The mortality rate of breast cancer can be reduced by early diagnosis and treatment. The computer-aided diagnosis (CAD) systems can effectively help physicians identify the early stage of breast cancer. The primary tool for such systems is mammograms. Usually, these images lack high quality. Furthermore, due to the irregular shape of masses, their size variability, and the apparent similarity of the masses and other dense regions of the breast tissue, it is difficult to detect and diagnose the masses. Although many image processing techniques have been presented for diagnosis of breast masses, they have not been quite successful, and this problem has been retained as a challenge yet. In this paper, a method for classifying breast tissues into normal and abnormal (i.e., cancerous) is proposed, which is based on a deep learning approach. It mainly contains a new convolutional neural network (CNN) and a decision mechanism. After a preprocessing phase, a block around each pixel is fed into a trained CNN to determine whether the pixel belongs to normal or abnormal tissues. This results in a binary map for the input suspicious tissue. Afterward, as a decision mechanism, a thresholding technique is applied to a central block on the produced binary map to label it. The new architecture of the CNN, the training scheme of the network, employing the CNN for classifying the pixels of the suspicious regions rather than the entire input, exerting an effective decision mechanism on the output of the CNN and the learning of the model parameters, helped us achieve superior results compared to state-of-the-art methods by reaching 95 and 94.68 percent for AUC and accuracy, respectively.

[1]  Zohreh Azimifar,et al.  Contourlet-based mammography mass classification using the SVM family , 2010, Comput. Biol. Medicine.

[2]  Yudy Purnama,et al.  Mammogram Classification using Law's Texture Energy Measure and Neural Networks , 2015 .

[3]  Amir Hussain,et al.  Local energy-based shape histogram feature extraction technique for breast cancer diagnosis , 2015, Expert Syst. Appl..

[4]  Qaisar Abbas,et al.  DeepCAD: A Computer-Aided Diagnosis System for Mammographic Masses Using Deep Invariant Features , 2016, Comput..

[5]  Yudong Zhang,et al.  Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling , 2018, J. Comput. Sci..

[6]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[7]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[8]  Hiba Chougrad,et al.  Deep Convolutional Neural Networks for breast cancer screening , 2018, Comput. Methods Programs Biomed..

[9]  Huai Li,et al.  A multiple circular path convolution neural network system for detection of mammographic masses , 2002, IEEE Transactions on Medical Imaging.

[10]  Samir Kumar Bandyopadhyay,et al.  Technique for preprocessing of digital mammogram , 2012, Comput. Methods Programs Biomed..

[11]  Lekha S. Nair,et al.  An efficient CAD for detection of tumour in mammograms using SVM , 2017, 2017 International Conference on Communication and Signal Processing (ICCSP).

[12]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[13]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[15]  R. Besar,et al.  Biomedical Imaging and Intervention Journal Identification of Masses in Digital Mammogram Using Gray Level Co-occurrence Matrices , 2022 .

[16]  D. Dance,et al.  Automatic computer detection of clustered calcifications in digital mammograms , 1990, Physics in medicine and biology.

[17]  M. Arfan,et al.  Deep Learning based Computer Aided Diagnosis System for Breast Mammograms , 2017 .

[18]  Daniel L. Rubin,et al.  Probabilistic visual search for masses within mammography images using deep learning , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[19]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[20]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[21]  T K Lau,et al.  Automated detection of breast tumors using the asymmetry approach. , 1991, Computers and biomedical research, an international journal.

[22]  N. Mathur,et al.  Biomonitoring of organochlorines in women with benign and malignant breast disease. , 2005, Environmental research.

[23]  Xinbo Gao,et al.  A deep feature based framework for breast masses classification , 2016, Neurocomputing.

[24]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[25]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[26]  Murray H. Loew,et al.  Breast Cancer Detection Using Transfer Learning in Convolutional Neural Networks , 2017, 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[27]  Zhenhua Guo,et al.  Rotation invariant texture classification using LBP variance (LBPV) with global matching , 2010, Pattern Recognit..

[28]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[29]  J. Samuel Manoharan,et al.  A Survey on the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer , 2011 .

[30]  Rangaraj M. Rangayyan,et al.  Statistical measures of orientation of texture for the detection of architectural distortion in prior mammograms of interval-cancer , 2012, J. Electronic Imaging.

[31]  Richa Singh,et al.  Saliency based mass detection from screening mammograms , 2014, Signal Process..

[32]  Heng-Da Cheng,et al.  Approaches for automated detection and classification of masses in mammograms , 2006, Pattern Recognit..

[33]  Samuel Cheng,et al.  Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study , 2016, SPIE Medical Imaging.

[34]  Ezzeddine Zagrouba,et al.  Breast cancer diagnosis in digitized mammograms using curvelet moments , 2015, Comput. Biol. Medicine.

[35]  Nello Cristianini,et al.  Latent Semantic Kernels , 2001, Journal of Intelligent Information Systems.

[36]  Shyr-Shen Yu,et al.  Automatic detection of abnormal mammograms in mammographic images , 2015, Expert Syst. Appl..

[37]  Omidiora Elijah Olusayo,et al.  Curvelet Transform-Local Binary Pattern Feature Extraction Technique for Mass Detection and Classification in Digital Mammogram , 2018 .

[38]  Abhishek Midya,et al.  Computer-aided detection and diagnosis of mammographic masses using multi-resolution analysis of oriented tissue patterns , 2018, Expert Syst. Appl..

[39]  Ioan Buciu,et al.  Directional features for automatic tumor classification of mammogram images , 2011, Biomed. Signal Process. Control..

[40]  Ronald Davis,et al.  Neural networks and deep learning , 2017 .

[41]  Amit Kumar Singh,et al.  Computer aided detection of mammographic mass using exact Gaussian–Hermite moments , 2018, Journal of Ambient Intelligence and Humanized Computing.

[42]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[43]  Nader Karimi,et al.  Abnormality detection of mammograms by discriminative dictionary learning on DSIFT descriptors , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[44]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[45]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..

[46]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.