Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning

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1 Amercan Journal of Appled Scences Orgnal Research Paper Classfcaton of Breast Tumor n Mammogram Images Usng Unsupervsed Feature Learnng 1 Adarus M. Ibrahm, 1 Baharum Baharudn, 1 Abas Md Sad and 2 P.N. Hashmah 1 Department of Computer and Informaton Scences, Faculty of Scence and Informaton Technology, Unverst Teknolog PETRONAS, 31750, Tronoh, Perak, Malaysa 2 Faculty of Computng and Mathematcal Scences, Unverst Teknolog MARA, Malaysa Artcle hstory Receved: Revsed: Accepted: Correspondng Author: Adarus M. Ibrahm Department of Computer and Informaton Scences, Faculty of Scence and Informaton Technology, Unverst Teknolog PETRONAS, 31750,Tronoh, Perak, Malaysa Emal: adarus_g02440@utp.edu.my Abstract: In ths study, we propose a learnng-based approach usng feature learnng to mnmze the manual effort requred to extract features. Frstly, we extracted features from equally spaced sub-patches coverng the nput Regon of Interest (ROI). The dmensonalty of the extracted features s reduced usng max-poolng. Furthermore, sphercal k-means clusterng coupled wth max poolng (k-means-max poolng) s compared wth wellknown feature extracton method namely Bag-of-features. The resultng feature vector s fed to two dfferent classfers: K-Nearest Neghbor (K- NN) and Support Vector Machne (SVM). The performance of these classfers s evaluated to use of Recever Operatng Characterstcs (ROC). Our results show that k-means-max poolng, combned wth K-NN, acheved good performance wth an average classfcaton accuracy of 98.19%, senstvty of 97.09% and specfcty of 99.35%. Keywords: Mammogram, Breast Cancer, K-Means Clusterng, Max- Poolng, Bag-of-Features, Classfer Introducton Breast cancer s dsease that s dagnosed among the women n the world (Marrocco et al., 2010). Mammography s used as early screenng tool for breast cancer. The studes n (Perlmutter et al., 1997; Rawashdeh et al., 2013; Rojas Domínguez and Nand, 2008), reported that the radologst may over look some abnormaltes durng mammogram mage screenng. Hence, Computer Aded (CAD) system helps the radologst to double check the mammogram mages. A lot of lterature has been revewed n the area of CAD systems for breast cancer as well as technques for mprovng classfcaton accuracy (Etehadtavakol et al., 2013; Korkmaz and Korkmaz, 2015; Pak et al., 2015; Ramrez-Vllegas and Ramrez-Moreno, 2012; Wang et al., 2014). Feature representaton s one of the technques that were developed for mprovng the classfcaton accuracy of CAD. Feature representaton contans set of technques that converts the nput data nto more meanngful representaton so that machne learnng algorthms can smply use t. Tradtonal manually desgned feature descrptors n (Yadav et al., 2015), such as gradent operators and flter banks, are not able to capture f complex varaton related n frequency s found n medcal mages (Rose et al., 2010). Ths paves the way for desgnng effcent mage descrptors. The process of learnng features concentrates nto man categores namely supervsed and unsupervsed learnng. In the supervsed learnng algorthms, usually t performs the process of learnng usng classes or targets. However, unsupervsed learnng methods, the process of feature learnng s carred out wthout usng any classes, (Hnton et al., 2006) (Maral et al., 2009), autoencoders (Boureau and Cun, 2008), (Coates et al., 2011). To the best of our knowledge, lmted work has been done on applyng unsupervsed feature learnng to mammogram mages. In ths study, we adopted the unsupervsed feature learnng process based on unlabeled data (Coates and Ng, 2012). Ths approach can learn mportant and subtle features from the statstcs of the mage. By applyng set of learned features coupled wth labels to tran the mammogram mages, the features were extracted from patches of sze 8 8 that represent the ROI of mammogram mages. Moreover, the obtaned features from the patches were pooled together to reduce the dmenson of the features. Fnally, classfcaton s performed to classfy between normal and abnormal usng the features obtaned from the unsupervsed learnng algorthm Adarus M. Ibrahm, Baharum Baharudn, Abas Md Sad and P.N. Hashmah. Ths open access artcle s dstrbuted under a Creatve Commons Attrbuton (CC-BY) 3.0 lcense.

2 Fg. 1. Feature learnng ppelne The man contrbuton of ths paper s the applcaton of k-means-max poolng n mammogram mages for automatc feature representaton to enhance the classfcaton of the mammogram mages nto normal and abnormal, as shown n Fg. 1. Materals and Methods The Dgtal Database for Screenng Mammography (DDSM) s a publcly-avalable resource used by the mage analyss communty (Heath et al., 2000). In ths experment, we used a total of 400 mages, whch represented 200 normal condtons and 200 abnormal (bengn and malgnant) condtons. The croppng operaton was appled to the mages to cut off the unwanted portons. ROIs were cropped to the sze of as shown n Fg. 2. The mammogram mages were cropped manually by selectng the ROI of mammogram mages. All the unnecessary parts such as background whch are out of the tumor area were completely elmnated. Feature Learnng Archtecture The ROIs obtaned from mammogram mages were transformed nto patches. Each patch of sze of 8x8 were collected and stored as feature vector. However, mammogram mages taken durng the breast cancer screenng mght have varatons such as brghtness and contrast. Hence, to elmnate ths ssue mage normalzaton. The learnng archtecture proposed by (Coates and Ng, 2012) uses the followng procedure for feature learnng representaton of an mage patch: Normalze for each patch, subtract out the mean of the ntenstes and dvde by the standard devaton as follows: x x = where, ( ) ( ) ~ ~ mean x ~ ( ) var( ) x + 10 (1) ~ ( ) x are normalzed parches and mean and var are the mean and varance of the elements of Whtenng Transform ~ ( ) x. Prncpal Component Analyss (PCA) s used to reduce the dmensonalty of the data (Kambhatla and Leen, 1997). There s a smlar preprocessng step called ZCA whtenng (Coates and Ng, 2011), whch s requred for some algorthms. If we are tranng on mages, the raw nput s redundant, snce neghborng pxel values are hghly correlated. The am of whtenng s to make the nput less redundant: T [ V, D] : = egh( cov ( x) ); So SDV / / = cov( x) 1 2 T x : = V ( D+ I) V x, zca (2) If VDV T = cov(x) s the egenvalue decomposton of the covarance of the data ponts, x, then the whtenng ponts are decomposed as V(D+ zca I) -1/2 V T x, where zca s constant. In ths study, to normalze the data, we set zca as 0.01 for 8 by 8 pxel patches. Sphercal K-Means Clusterng Coates and Ng (2012), make a convncng case that K-means clusterng s capable of learnng dctonares that can be easly used for classfcaton. The K-means algorthm s partcularly ntrgung and t s very fast compared to standard K-means usng Eucldean dstances. Followng the steps of a sphercal K-means algorthm, whch s much faster than usng the conventonal K-means (Zhong, 2005). 553

3 (a) (b) Fg. 2. Sample mammogram mages: (a) normal mages, (b) abnormal mages Max Poolng Max poolng makes the feature learnng nto new reusable features that keeps sgnfcant nformaton although removng redundant nformaton. The typcal propertes of poolng are the robustness to cluster and compactness of representaton (Boureau et al., 2010). In ths study, the followng poolng steps were appled. Notatons: For nstance, f the unpooled data are a p K matrx of 1-of-k codes taken at P locatons, we extract a sngle P-dmensonal column v of 0s and 1s, ndcatng the absence or presence of the feature at each locaton 554

4 The vector v s reduced by a poolng operaton to a sngle scalar f(v) 1 p = p Max poolng: fa( v) 1 = average poolng : f m (v) = max v Gven two classes C 1 and C 2, we examne the separaton of condtonal dstrbutons: ( m 1) ( m 2), ( a 1) ( a 2) p f C and p f C p f C and p f C (3) In ths expermental desgn, we take the max of the cluster membershps over each 8x8 regon. Max poolng reduces the dependency of the feature vectors on ther exact placement n an mage (each element of each 8x8 block gets treated about the same) and t also mantans a lot of the nformaton that was n each of the feature vectors, especally when the feature vectors are expected to be sparse. Moreover, durng the experment desgn, we have taken dfferent sze of clusters (100 clusters and 150 clusters) to fgure out the performance of the classfers, as shown n secton 3. Bag-of-Features Bag-of-features s s a vector of occurrence counts of a vocabulary of local mage features. The basc steps of bag-of-words when appled to mages are as follows (Cheng et al., 2010): Buldng a codebook for local patches Extractng the local patches from the ROIs of mammogram mages representng an ROIs usng the statstcs of ts quantzed local patches Inference based on the statstcs collected n step 3 The obtaned set of ROI of mammogram mages s represented and dvded nto testng and tranng sets. Then, vsual vocabulary s bult by clusterng the patches from the tranng set followed by representng them as per mage dstrbutons. Moreover, each patch s represented as hstogram of vsual words drawn from vocabulary. In The expermental setup, 100 clusters and 150 clusters based on k-means as clusterng method was appled to evaluate the classfer performance. Classfcaton The learned features were stored as feature vector. The next step s to separate gven classes nto normal and abnormal. Two well know classfers are adopted n ths study as follows: K-Nearest Neghbor The KNN classfer usually apples ether the Eucldean dstance or the cosne smlarty between the v tranng tuples and the test tuples. In ths study, the Eucldean dstance s appled n mplementng the KNN(k = 1) model for feature classfcaton (Cunnngham and Delany, 2007). Support Vector Machne A Support Vector Machne (SVM) s wdely used n mammogram mages classfcaton due to ts performance for the accuracy rate. Ths classfer acheves the classfcaton rate by applyng hyperplane. To see the classfcaton performance of SVM for separatng the two classes (normal Vs abnormal) and t s recommended the hyperplane has the largest dstance to the nearest tranng data pont of the two classes (Chang and Ln, 2011). Performance Measures The performance of the classfer s evaluated usng a ten-fold cross valdaton method (Acharya et al., 2015). The dataset s dvded nto ten equal parts. Each part contans the features from a smlar proporton of mages from normal and abnormal classes. Nne parts are used to tran the classfers. The remanng parts of the mage features are used as a testng set. Ths process s repeated ten tmes usng a dfferent set n each case. In each fold, we apply several classfcaton measures n order to obtan a more relable comparson. Normal and abnormal mammographc mages, respectvely correspond to negatve and postve samples. In ths study, True Postve (TP) and True Negatve (TN), respectvely represent the number of abnormal and normal tests, whch are properly classfed. Smlarly, False Postve (FP) and False Negatve (FN), respectvely, represent the number of normal and abnormal tests, whch are ncorrectly classfed. The mathematcal computaton of performance measures are as follows: TP+ TN Accuracy = 100 (4) N TP Senstvty= TP+ FN TN Specfcty = TN + FP (5) (6) TP+ TN Precson= (7) N Recever Operatng Characterstcs The Recever Operatng Characterstcs (ROC) curve provdes a vsual representaton of the tradeoff between true postve and false postves. They are the 555

5 percentage of correctly classfed features wth respect to the percentage of ncorrectly classfed negatve features (Nascmento et al., 2013). As shown n Fg. 4 to 7, the pont (0,0) along curve represents a classfer that by default classfes all features as beng negatve, where a pont (0,1) represent a classfer that postvely classfes all features. The expermental results s carred out usng wndows 8 and MATLAB R2013a. Some of the tools that were selected were mage processng and statstcal tool box wth applcaton of Laptop of ntel Core U Results Table 1 to 4 presents the classfcaton results usng KNN and SVM classfers for k-means-max poolng and Bag-of-features. We have obtaned the hghest performance for k-means-max poolng as compared to Bag-of-features method wth an average accuracy, senstvty and specfcty of 98.19, and 99.35% respectvely (at 150 clusters). It s also obvous from the results that the KNN classfer performs better than SVM, wth a hgh accuracy of 98.19%. To get the best optmum classfer wth a sngle dataset, the classfer should not only have sgnfcant accuracy, but should also provde good senstvty and specfcty. Such a balance s requred to make certan decsons that classfy two classes (normal Vs. abnormal). Hence, ths K-NN classfer was selected as the optmum classfer for ths dataset manly because t provded the best accuracy, senstvty and specfcty. Fgure 3 vsualzes the resultng 50 clusters centers from k-means-max poolng. We sorted the data based on how often data centers are assgned to each cluster. So, the top left has the most elements and the bottom rght has the least. Fgure 4 to 7 show the ROC curve for k-means-max poolng compared wth Bag-of-features usng KNN and SVM. It can be observed from these fgures that the performance of k-means-max poolng s better n the KNN classfer at the 150 cluster. Furthermore, the ROC curve s more near to the y-axs for the KNN classfer than Bag-of-features, ndcatng a hgher AUC value for KNN. Therefore, k-means-max poolng coupled wth KNN has the hghest dscrmnaton capacty to separate the two classes (normal Vs. abnormal). Table 1. Performance of feature learnng at 100 clusters Performance Classfer Methods Accuracy (%) Senstvty (%) Specfcty (%) AUC (%) KNN K-means-max- poolng Bag-of-features Table 2. Performance of feature learnng at 150 clusters Performance Classfer Methods Accuracy (%) Senstvty (%) Specfcty (%) AUC (%) KNN K-means-max- poolng Bag-of-features Table 3. Performance of feature learnng at 100 clusters Performance Classfer Methods Accuracy (%) Senstvty (%) Specfcty (%) AUC (%) SVM K-means-max- poolng Bag-of-features Table 4. Performance of feature learnng at 150 clusters Performance Classfer Methods Accuracy (%) Senstvty (%) Specfcty (%) AUC (%) SVM K-means-max- poolng Bag-of-features

6 Fg. 3. Vsualzaton cluster centrods Fg. 4. ROC performance of KNN classfer (at 150) 557

7 Fg. 5. ROC performance of KNN classfer (at 100 clusters) Fg. 6. ROC performance of SVM classfer (at 150) clusters 558

8 Fg. 7. ROC performance of SVM classfer (at 100 clusters) Table 5. Summary of studes reportng automated detecton of breast tumors from mammographc mages Authors No. of Images Features Method used Dataset Classfer Accuracy (%) (Rocha et al., 2014) 300 mage Gray Level Co-occurrence Matrxes Gleason and Menhnck DDSM SVM 86 (GLCM), Gray Level Run Length dversty ndexes Matrxes (GLRLM) and Gray Level Gap Length Matrxes (GLGLM) (Lm and Er, 2004) 343 mages Frst-order gradent dstrbuton Frst-order gradent dstrbuton DDSM GDFNN 70 and Gray-level Co-occurrence and gray-level co-occurrence Matrces (GCMs) matrces Our proposed study 400 mages Unsupervsed feature learnng k-means-max poolng DDSM KNN, SVM Dscusson Selectng the sgnfcant features from mammogram mages s mportant for the mammogram mage classfcaton. Sgnfcant features ncreases the accuracy rate of the CAD system n breast cancer. The proposed methods to extract mportant features from mammogram mages vary from one another and they are mplemented ether n supervsed or unsupervsed learnng. In ths study, we take the unsupervsed feature learnng method that wll help n detectng breast tumors. We present the comparson of the results obtaned usng our technque and other technques n the lterature that also am to dagnose mammogram breast tumors. The summary of the studes reported by dfferent authors s shown n Table 5. Rocha et al. (2014) classfed 200 DDSM mammogram mages nto normal and malgnant classes usng Gleason and Menhnck dversty ndexes and the SVM classfer. They have reported senstvty and specfcty of 90 and 83.33%, respectvely. Lm and classfed 343 DDSM mammogram mages nto bengn and malgnant usng frst-order gradent dstrbuton, gray-level co-occurrence matrces and Generalzed Dynamc Neural Networks (GDFNN). They acheved a true-postve and false postve fractons of 95.0 and 52.8%, respectvely. The advantages of our proposed method are as follows: We reported the hghest senstvty of 97.09% and a specfcty of 99.35% n classfyng the normal and abnormal classes compared to Bag-of-features The proposed system s able to detect two classes (normal Vs abnormal) wth 98.19% accuracy; ths wll reduce the workload of clncans Our proposed method s more relable, as we used ten-fold stratfed cross-valdaton and used 400 mammogram mages n ths study Future Works In the future, ths study wll focus outsde the boundares of the skn whch s the area of cancer. By 559

9 performng possble multple selecton between bengn and malgnant Concluson An accurate and fast dagnoss of breast cancer can help clncans n ther dagnoss. Hence, n ths study, we proposed a CAD system based on k-means-max poolng for early detecton of breast cancer. We reported an accuracy of 98.19%, senstvty of 97.09% and specfcty of 99.35% usng the K-NN classfer for k-means-max poolng. We have also showed that, the features of k-means-max poolng performed better than Bag-of-feature. Our proposed system s able to clearly dagnose all normal and abnormal cases correctly (97.07% senstvty) and reduce clncan workloads. Acknowledgement The authors would lke to thank TM Lehmann, Dept. of Medcal Informatcs, RWTH Aachen, Germany and the Unversty of South Florda for makng the database avalable for research purposes. Author s Contrbuton All the authors have made same contrbuton n expermental results as well as wrtng the manuscrpt. Ethcs We want to declare that ths manuscrpt has not been publshed or s under consderaton elsewhere. References Acharya, U.R., E. Ng, L.W.J. Eugene, K.P. Noronha and L.C. Mn et al., Decson support system for the glaucoma usng Gabor transformaton. Bomed. Sgnal Process. Control, 15: DOI: /j.bspc Boureau, Y.L. and Y.L. Cun, Sparse feature learnng for deep belef networks. Proceedngs of the Advances n Neural Informaton Processng Systems, (IPS 08), pp: Boureau, Y.L., J. Ponce and Y. LeCun, A theoretcal analyss of feature poolng n vsual recognton. Proceedngs of the 27th Internatonal Conference on Machne Learnng, (CML 10), Israel, pp: Chang, C.C. and C.J. Ln, LIBSVM: A lbrary for support vector machnes. ACM Trans. Intell. Syst. Technol., 2: DOI: / Cheng, E., N. Xe, H. Lng, P.R. Bakc and A.D. Madment et al., Mammographc mage classfcaton usng hstogram ntersecton. Proceedngs of the IEEE Internatonal Symposum on Bomedcal Imagng: From Nano to Macro, Apr , IEEE Xplore Press, Rotterdam, pp: DOI: /ISBI Coates, A. and A.Y. Ng, Selectng receptve felds n deep networks. Proceedngs of the Advances n Neural Informaton Processng Systems, (IPS 11), pp: Coates, A. and A.Y. Ng, Learnng Feature Representatons wth k-means. In: Neural Networks: Trcks of the Trade, Montavon, G., G.B. Orr and K.R. Müller (Eds.), Sprnger, New York ISBN-10: , pp: Coates, A., A.Y. Ng and H. Lee, An analyss of sngle-layer networks n unsupervsed feature learnng. Proceedngs of the Internatonal Conference on Artfcal Intellgence and Statstcs, (AIS 11), Fort Lauderdale, FL, USA., pp: Cunnngham, P. and S.J. Delany, k-nearest neghbour classfers. Multple Classfer Syst. Etehadtavakol, M., E. Ng, V. Chandran and H. Rabban, Separable and non-separable dscrete wavelet transform based texture features and mage classfcaton of breast thermograms. Infrared Phys. Technol., 61: DOI: /j.nfrared Heath, M., K. Bowyer, D. Kopans, R. Moore and W.P. Kegelmeyer, The dgtal database for screenng mammography. Proceedngs of the 5th Internatonal Workshop on Dgtal Mammography, (WDM 00). Hnton, G.E., S. Osndero and Y.W. Teh, A fast learnng algorthm for deep belef nets. Neural Comput., 18: DOI: /neco Kambhatla, N. and T.K. Leen, Dmenson reducton by local prncpal component analyss. Neural Comput., DOI: /neco Korkmaz, S.A. and M.F. Korkmaz, A new method based cancer detecton n mammogram textures by fndng feature weghts and usng kullback-lebler measure wth kernel estmaton. Optk-Int. J. Lght Electron Opt., 126: DOI: /j.jleo Lm, W.K. and M.J. Er, Classfcaton of mammographc masses usng generalzed dynamc fuzzy neural networks. Med. Phys., 31: DOI: /

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