AN AUTOMATIC COMPUTER-AIDED WOMEN BREAST CANCER DIAGNOSIS SYSTEM FROM 2-D DIGITAL MAMMOGRAPHIC IMAGES

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1 International Journal of Computer Engineering & Technology (IJCET) Volume 9, Issue 4, July-August 2018, pp , Article ID: IJCET_09_04_013 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication. AN AUTOMATIC COMPUTER-AIDED WOMEN BREAST CANCER DIAGNOSIS SYSTEM FROM 2-D DIGITAL MAMMOGRAPHIC IMAGES Dr. A. Kalaivani Assistant Professor (SG), Computer Science and Engineering, Saveetha School of Engineering, Saveetha University D. Bhavitha BE Second Year, CSE Department, Saveetha School of Engineering, Saveetha Univerisity, Chennai, India R. Vijaya Lakshmi BE Second Year, CSE Department, Saveetha School of Engineering, Saveetha Univerisity, Chennai, India ABSTRACT Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate and ensure long survival of the patients. Diagnosis of breast cancer can be made from clinical examination based on the symptoms produced or based on the imaging analysis of digital medical images to identify abnormality in the to test for cancer tissue or not. The automatic prediction model can be a non-image based risk model based on genetic susceptibility factors or image-based prediction model based on Mammography, MRI, Ultrasound images. Mammography is most preferred as it uses a low dosage of x-rays and takes 20 second scan and suitable for fatty and dense breasts. The abnormalities in mammograms include micro-calcifications (MCs), masses, architectural distortion and bilateral asymmetry. The current automatic CAD systems suffer from false diagnosis which is resolved by the proposed system with improved accuracy helps the expert effectively and reduces their examination time. Keywords: Computer-Aided Diagnosis, Breast Cancer Leisons, Digital Mammography, Supervised Classifier, Feature Extraction. Cite this Article: Dr. A. Kalaivani, D. Bhavitha and R. Vijaya Lakshmi, An Automatic Computer-Aided Women Breast Cancer Diagnosis System from 2-D Digital Mammographic Images. International Journal of Computer Engineering & Technology, 9(4), 2018, pp editor@iaeme.com

An Automatic Computer-Aided Women Breast Cancer Diagnosis System from 2-D Digital Mammographic Images 1. INTRODUCTION Breast is the tissue overlying the chest muscles.

2 An Automatic Computer-Aided Women Breast Cancer Diagnosis System from 2-D Digital Mammographic Images 1. INTRODUCTION Breast is the tissue overlying the chest muscles. The shape of the breast varies between patients and understanding the anatomy of the breast ensures safe surgical planning. The breasts should be carefully examined for any preexisting asymmetries, spinal curvature, or chest wall deformities. Women's breast is made up of specialized tissue to produce milk and also includes fatty tissues. The breast size depends on the fat content and the specialized tissue is organized into 15 to 20 sections called lobes. The milk is produced in lobules which is the smaller structure available inside lobes. The milk travels through a network of tiny tubes called ducts which when connected forms larger ducts moves towards skin in the nipple. The dark area of skin surrounding the nipple is called the areola. The shape of the breast is decided on the connecting tissues, ligaments and a nerve provides sensation to the breast. The breast also contains blood vessels, lymph vessel and lymph nodes. Breast cancer is a disease originating from the breast tissue from the inner lining of the milk ducts or lobules supplying the ducts with milk. Breast cancer occurs in both men and women but breast cancer for men is rare than women. Breast cancer ranked number one cancer in women around the world. Even with enhanced treatment the women are higher risk of dying from this disease. The survival rate of breast cancer depends on the stages of cancer. Table 1 shows the 5-year survivability rate of a cancer patient. Table 1 Breast Cancer Survivability Rate Stages Description 5-year 10-year survival survival (%) (%) Stage-0 No evidence of Primary Tumor Stage-1 Tumor<=2cm Stage-2 Tumor> 2cm and Tumor<=5cm Stage-3 Tumor> 5cm Stage-4 Any size extending to chest wall or skin 5 2 Clinical diagnosis of breast cancer helps in predicting the breast cancer in women. A lump felt during examination gives clues to tumor size and texture. Various common methods used for breast cancer diagnosis are Mammography, Positron Emission Tomography, Biopsy and Magnetic Resonance Imaging. The results obtained from these methods are used to recognize the pattern which helps doctors for classifying the malignant and benign cases. Full-field Digital mammography system in which the X-ray film is replaced by electronics which convert X-rays into mammographic pictures of the breast enables better picture with a lower radiation dose. The breast images are transferred to a computer for review by the radiologist and for long term storage. Figure 1 Digital mammography system (from (Bronzino, 2000)) editor@iaeme.com

Dr. A. Kalaivani, D. Bhavitha and R. Vijaya Lakshmi 2. BREAST CANCER LESIONS Breast cancer includes lesions such as micro-calcifications, masses, architectural distortions and bilateral asymmetry. 2.1.

3 Dr. A. Kalaivani, D. Bhavitha and R. Vijaya Lakshmi 2. BREAST CANCER LESIONS Breast cancer includes lesions such as micro-calcifications, masses, architectural distortions and bilateral asymmetry Micro-calcifications Micro-calcifications are small deposits of calcium of size from 0.33 to 0.7 mm which are slightly brighter than surrounding tissues. By using mammogram these lesions are difficult to detect as they appear with low contrast due to their small size. Associated with extra cell activity in breast tissue micro-calcifications can be identified in clusters or as patterns. A micro-calcification cluster is more detectable than an isolated micro-calcification which signifies the diagnosis of early stages of breast cancer. Micro-calcification should distinguish between benign and malignant micro-calcifications specified by (Kavitha, 2007; Rovere, 2006). Table 2 Grading Micro-calcification ( Rovere, 2006) Grade Degree of suspicion Mammographic appearance 1 Normal No abnormality seen 2 Consistent with a benign lesion Popcorn, ring, micro cystic or diffuse bilateral calcification 3 A typical or indeterminate but Localized cluster of round, fine or punctuate probably benign calcification 4 Suspicious of malignancy Localized cluster of granular calcification 5 Consistent with malignancy Comedo Calcification 2.2. Masses Masses are lesions which is more difficult to detect in monographs than micro-calcifications because due to the resemblance of the normal breast parenchyma. The shape of the mass can be round, oval, lobulated or irregular and margins can be from circumscribed to speculate (Jatoi 2010). Figure 2 Mass Description (Jatoi, 2010) When a mass is detected, benignant or malignant cancer can be differentiated based on the features of shape and texture between them. Benign masses are smooth and distinct and are round in shape. The malignant masses are irregular and their boundaries are usually blurry. A sample figure is shown in fig 2. A mass with regular shape has a higher probability of being benign whereas a mass with an irregular shape has a high probability of benign or malignant editor@iaeme.com

4 An Automatic Computer-Aided Women Breast Cancer Diagnosis System from 2-D Digital Mammographic Images 2.3. Architecture Distortion The breast contains several piecewise linear structures, such as ligaments, ducts, and blood vessels, that cause directionally oriented texture in mammograms. The presence of architectural distortion is expected to change the normal oriented texture of the breast. Characterization of such subtle changes from a pattern recognition perspective is the goal of many approaches. Figure 3 Mass Description (Jatoi, 2010) 2.4. Bilateral Symmetry Asymmetrical breasts are the reliable indicators of future breast disease in women which is considered as a woman's risk profile. Breasts which are changing or enlarging or are new which are palpable and with other findings are micro-calcifications or architectural distortion. During years many researchers dedicated themselves to study intensively breast cancer lesions. Some of them invested in work in only one or two stages, and others created a complete CAD system giving one more advance in this area. Micro-calcifications and masses are the most common lesions of the interest for researchers may be because the challenge existing in this problem. However, there exists some literature related with architectural distortions and bilateral asymmetries. Hence, the development of new breast cancer computer-aided detection is an active research field, particularly regarding the detection of subtle abnormalities in mammograms. 3. LITERATURE SURVEY A great amount of research on breast cancer datasets are done by researchers and the work contributed in the area of computer aided diagnosis for breast cancer are discussed below.muhammad Talha et al, proposed mammogram classification of breast cancer using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) features as an input to the classifier. The proposed method gives 96.97% accuracy, 98.39% sensitivity and 94.59% specificity for classifying mammograms into normal and abnormal (cancer) categories using SVM (Support Vector Machine) classifier for MIAS (Mammographic Institute Society Analysis) dataset. Abdelali el Moulfi et al., proposed method to detect the suspicious lesions in mammograms, by extracting their features to classify breast cancer tissue as Normal or Abnormal and Benign or Malignant breast cancer. The proposed method A great amount of research on breast cancer datasets are done by researchers and the work contributed in the area of computer aided diagnosis for breast cancer are discussed below. Muhammad Talha et editor@iaeme.com

5 Dr. A. Kalaivani, D. Bhavitha and R. Vijaya Lakshmi al, proposed mammogram classification of breast cancer using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) features as an input to the classifier. The proposed method gives 96.97% accuracy, 98.39% sensitivity and 94.59% specificity for classifying mammograms into normal and abnormal (cancer) categories using SVM (Support Vector Machine) classifier for MIAS (Mammographic Institute Society Analysis) dataset. Abdelali el Moulfi et al., proposed method to detect the suspicious lesions in mammograms, by extracting their features to classify breast cancer tissue as Normal or Abnormal and Benign or Malignant breast cancer. The proposed method produced 94.29% accuracy, w94.11% sensitivity, 94.44% specificity for mammogram classification, and has achieved 94.4% accuracy, with 96.15% sensitivity and 94.54% specificity in the classification as Benign or Malignant mammogram.saranya R et al., identify micro calcification as a white speckles on mammographic images. The mammorgraphic images are enhanced and subsequently features are extracted from the suspicious region. The proposed method produced great accuracy and reliability to detect whether micro calcification is malignant or not. R.Pavitha proposed a wavelet based threshold segmentation method, for segmenting mammographic images for detecting breast cancer in its early stages. The artificial neural network will be used to classify the mammographic images classified as abnormal or normal. The Computer Aided Diagnosis system is used for early detection of cancer from mammographic images which will improves the chances of survival for the patient. M.Sadhana research focus to find out the best classifier with respect to accuracy on three different databases of breast cancer (Wisconsin Breast Cancer (WBC), Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Prognosis Breast Cancer (WPBC). A classification accuracy method SVM classifier used to produce a classifier accuracy of 96.99%. J. Dheeba proposed a classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed method extracts Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The research work shows that the area under the ROC curve is with a sensitivity % and specificity of %. Gouda I. Salamaimplemented a research work on comparison of different classifiers decision tree (J48), Multi-Layer Perception (MLP), Naive Bayes (NB), Sequential Minimal Optimization (SMO), and Instance Based for K-Nearest neighbor (IBK). The test is carried on three different databases of breast cancer WBC, WDBC, WPBC. The proposed algorithm is based on classification accuracy and confusion matrix based on 10-fold cross validation method. A fusion at multi classifier approach is also used for each data sets and a fusion of MLP and J48 is superior to the other classifiers using WBC data set. From the papers surveyed, it is identified that computer aided diagnosis model is required for diagnosis of breast cancer. The model should be designed in such a manner that classifier should produce improved accuracy and reduced error measures. 4. MATERIALS AND METHODS Computer Aided Diagnosis systems perform automatic assessments of patient images and present to radiologist towards the appearance of an abnormality. It is believed that CAD could be an effective and efficient solution for implementing double reading, which provides double perception and interpretation. Technicians can be attracted by some features and can miss a lesion which can be used to identify disease. The CAD systems read images faster without reducing accuracy, what not happens with radiologists, but medical community needs to be confidant in the results editor@iaeme.com

An Automatic Computer-Aided Women Breast Cancer Diagnosis System from 2-D Digital Mammographic Images The automatic computer aided diagnosis of breast cancer is an important real-world medical

6 An Automatic Computer-Aided Women Breast Cancer Diagnosis System from 2-D Digital Mammographic Images The automatic computer aided diagnosis of breast cancer is an important real-world medical problem which investigated the work in one or two stages, and others created a complete CAD system. Hence, the development of new breast cancer computer-aided diagnosis system is an active research field, particularly regarding the detection of subtle abnormalities in mammograms to improve the accuracy and efficiency of radiologists. Mammograms are the images but difficult to interpret, and a mammographic images are preprocessed to improve the quality of the images and make the feature extraction phase more reliable. The image enhancement technique is applied to enhance the quality of the images. After preprocessing phase, features relevant to the classification are extracted from the cleaned images in which the most relevant and non-redundant features are selected for the classification model to develop as shown in Fig 4. The breast medical images used in the experiments were taken from the UCI Machine Repository. It consists of 569 images, corresponding to two categories: benign and malign. There are 357 benign and 212 malignant images. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. The total numbers of instances are 569 and the number of attributes extracted from the image is 32 which include ID, diagnosis and 30 real-values input features. Features are extracted from each cell nucleus as radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, and symmetry and fractal dimensions. The mean, standard error and largest (mean of the three largest values) of these features were computed for each image of 30 features. Figure 4 CAD model for Breast Cancer Diagnosis 4.1. Experimental Results We have used the Weka toolkit to experiment the breast cancer dataset captured through digital mammography used to evaluate the performance and effectiveness of the breast cancer prediction models. The various Feature Methods chosen such as fssubset evaluation, filtered subset evaluation, Gain Ratio, Chisquare, SVM Attribute, Relief Attribute with their corresponding search methods such as Best first and Ranker methods. The features selected and the features subjects are listed in the table 3. The classifier model is built for the chosen feature subset such as J48 classifier, NNGe, IB1 supervised classifier. The performance of a chosen classifier is validated based on error rate and computation time. The classification accuracy is predicted in terms of Precision, Recall and F-Score. The evaluation parameters are the correctly classified instance (CCI) and in-correctly classified instance (ICCI). The error measures recorded for the classifier performance are mean absolute error (MAE) and root mean square error (RMSE). The performance metrics of CCI and ICCI for Decision Tree J48 supervised classifier are listed in Table. The classifier accuracy in terms of precision, recall and F-score and the error measures are listed in Table editor@iaeme.com

7 S.Nos. Feature Methods Dr. A. Kalaivani, D. Bhavitha and R. Vijaya Lakshmi Table 3 Optimal Feature Search Method Features Selected Features Subset 1 Cfssubseteval Bestfirst 2,7,8,14,19,21,23,24,25,27, Filtered Subset Evaluation Bestfirst 3,8,9,15,22,24,25,28, GainRatio Ranker 24,22,25,29,9,8,28,4,5,2,15,7,12,14, Chisquare Ranker 24,22,25,29,9,4,5,2,8,15,28,12,14,27, SVM Attribute Ranker 22,29,24,23,9,25,30,2,26,5,12,3,4,8, Relief Attribute Ranker 22,29,24,23,2,4,25,9,5,8,3,28,26,12,27 15 S.No. Feature Methods Table 4 Decision Tree J48 Supervised Classifiers Features Subset Performance Accuracy Error Measures CCI ICCI Precision Recall F-score MAE RMSE 1 All Features Cfssubseteval Filtered Subset Evaluation GainRatio Chisquare SVM Attribute Relief Attribute The performance metrics of CCI and ICCI for NNGE supervised classifier are listed in Table. The classifier accuracy in terms of precision, recall and F-score and the error measures are listed in Table 5.The performance metrics of CCI and ICCI for Lazy Learner IB1 supervised classifier are listed in Table. The classifier accuracy in terms of precision, recall and F-score and the error measures are listed in Table 6. S.No. Feature Methods Table 5 NNGe Supervised Classifier Features Subset Performance Accuracy Error Measures CCI ICCI PrecisionRecall F-score MAE RMSE 1 All Features Cfssubseteval Filtered Subset Evaluation GainRatio Chisquare SVM Attribute Relief Attribute editor@iaeme.com

S.No. An Automatic Computer-Aided Women Breast Cancer Diagnosis System from 2-D Digital Mammographic Images Feature Methods Table 6 Lazy Learner IB1 Supervised Classifiers Features Subset Performance

8 S.No. An Automatic Computer-Aided Women Breast Cancer Diagnosis System from 2-D Digital Mammographic Images Feature Methods Table 6 Lazy Learner IB1 Supervised Classifiers Features Subset Performance Accuracy Error Measures CCI ICCI Precision Recall F-score MAE RMSE 1 All Features Cfssubseteval Filtered Subset Evaluation GainRatio Chisquare SVM Attribute Relief Attribute Comparative Analysis The comparative performance measure of J48, NNGe and IB1 classifier performance measures of CCI and ICCI are shown in the table 7 and the corresponding comparative graph is shown in figure 5. Table 7 Comparative Performance Analysis CCI, ICCI of Supervised Classifiers S.Nos. Feature Methods CCI ICCI 1 All Features Gain Ratio Features + J48 Classifier Gain Ratio Features + NNGe Classifier Gain Ratio Features + IB1 Classifier Figure 5 Comparative Performance Chart of Supervised Classifiers The comparative performance measure of J48, NNGe and IB1 classifier accuracy measures of precision, recall and f-score are shown in the table 8 and the corresponding comparative graph is shown in fig 6. Table 8 Comparative Accuracy Measures of Supervised Classifiers S.Nos. Feature Methods Precision Recall F-Score 1 All Features Gain Ratio Features + J48 Classifier Gain Ratio Features + NNGe Classifier Gain Ratio Features + IB1 Classifier editor@iaeme.com

shown in the table 9 and the corresponding comparative graph is shown in fig 7. Table 9 Comparative Error Measures of Supervised Classifiers S.Nos. Feature Methods MAE RMSE 1 All Features 0.07 0.26 2.

9 Dr. A. Kalaivani, D. Bhavitha and R. Vijaya Lakshmi Figure 6 Comparative Accuracy Chart of Supervised Classifiers The comparative performance measure of J48, NNGe and IB1 classifier accuracy measures of precision, recall and f-score are shown in the table 9 and the corresponding comparative graph is shown in fig 7. Table 9 Comparative Error Measures of Supervised Classifiers S.Nos. Feature Methods MAE RMSE 1 All Features Gain Ratio Features + J48 Classifier Gain Ratio Features + NNGe Classifier Gain Ratio Features + IB1 Classifier Figure 7 Comparative Error Measures of Supervised Classifiers From the experimental results analyzed, Gain ratio feature subset selection method produces the optimal features of 12 features from the total features of 30 features. The optimal subset features produces the better classifier accuracy results with supervised classifiers. When compared with three supervised classifier J48, NNGe, IB1 classifier based on classifier performance, accuracy and error measures J48 decision tree classifier produces out performing results with other supervised classifier. So, it is concluded that J48 supervised classifier seems to the better model for diagnosis of breast cancer, diagnosis the disease 97 % correctly and 3% only produces wrong diagnosis. 5. CONCLUSION In this chapter, a computer diagnosis model has been devised for diagnosis of breast cancer and their performance also measured and compared with other supervised classifier model. The produced classification results are very much promising The produced classification results are very much promising with 97% accuracy of correct classification and F-Score is of 93 % with reduced error measures is of 4%. The proposed method may provide an adequate support to the radiologists in differentiating between normal and abnormal breast cancer identification with high accuracy and of low error measures. The research can be focused further to develop better preprocessing, enhancement and segmentation techniques. It can be expanded to design better feature extraction, selection and classification algorithms; integration of classifiers to reduce both false positives and false negatives; employing high resolution mammograms and investigating 3D mammograms editor@iaeme.com

10 An Automatic Computer-Aided Women Breast Cancer Diagnosis System from 2-D Digital Mammographic Images REFERENCES [1] Abdelali el Moulfi, K halid E l Fahssi, Said J ai-andaloussi, A bderrahim S ekkaki, G wenolequellec, Mathieu, Lamard, Guy Cazuguel. Automatic Diagnosing of Suspicious Lesions in Digital Mammograms, International Journal of Advanced Computer Science and Application. 7(5), (2016.) [2] Dheeba,J.AlbertSingh,N. Tamilselvi,S. Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. Journal of Biomedical informatics.49, 45-52, (2014). [3] Frank, A. & Asuncion, UCI Machine Learning Repository Irvine CA: University of California, School of Information and Computer Science, from (2010) [4] Gouda I. Salama, M.B.Abdelhalim, MagdyAbd-elghanyZeid. (2012). Breast Cancer Diagnosis on ThreeDifferent Datasets Using Multi-Classifiers. International Journal of Computer and Information Technology, 1(1) [5] Holmes, G., Donkin, A., Witten, I.H. WEKA: a machine learning workbench. In: Proceedings Second Australia and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia, , (1994). [6] Jatoi, I., & Kaufmann, M. Management of breast diseases Springer, Edition. (2010). [7] Karthikeyan Ganesan, Rajendra Acharya, U. Eng, D. Chua Kuang Chua, Lim Choo Min, Thomas K. Abraham Automated Diagnosis of Mammogram Images of Breast Cancer Using Discrete Wavelet Transform and Spherical Wavelet Transform Features: A Comparative Study. Technology in Cancer Research and Treatment, 13(6), (2014). [8] Muhammad Talh. Classification of mammograms for breast cancer detection using fusion of discretecosine transform and discrete wavelet transform features. Biomedical Research, 27(2), (2016). [9] Nalini Singh, Ambarish G Mohapatra, Biranchi Narayan Rath, and Guru KalyanKanungo. GUI BasedAutomatic Breast Cancer Mass and Calcification Detection in Mammogram Images using K-means and Fuzzy C- means Methods. International Journal of Machine Learning and Computing, 2(1), (2012). [10] [10] Pavitha,R. Ms T.JoyceSelva Hephzibah. Mammographic Cancer Detection and Classification UsingBi Clustering and Supervised Classifier. International Journal of Innovative Research in Science, Engineering and Technology, 3(1), (2014). [11] Rovere, G. Q. della, Warren, R., & Benson, J. R. Early Breast Cancer: From Screening to Multidisciplinary Management (2nd edition). Taylor & Francis. (2006). [12] Sadhana,M. Sankareswari,A. A Proportional Learning of Classifiers using Breast Cancer Datasets, International Journal of Computer Science and Mobile Computing.3(11) (2014). [13] Saranya R, Bharathi,M. Showbana.R. Automatic Detection and Classification of Microcalcification on Mammographic Images. IOSR Journal of Electronics and Communication Engineering, 9(3), (2014). [14] Sawyer, S., & Tapia, A. The sociotechnical nature of mobile computing work: Evidence from a studyof policing in the United States. International Journal of Technology and Human Interaction, 1(3), 1-14, (2005) editor@iaeme.com

Mammography is a most effective imaging modality in early breast cancer detection. The radiographs are searched for signs of abnormality by expert

Mammography is a most effective imaging modality in early breast cancer detection. The radiographs are searched for signs of abnormality by expert Abstract Methodologies for early detection of breast cancer still remain an open problem in the Research community. Breast cancer continues to be a significant problem in the contemporary world. Nearly