A REVIEW ON CLASSIFICATION OF BREAST CANCER DETECTION USING COMBINATION OF THE FEATURE EXTRACTION MODELS. Aeronautical Engineering. Hyderabad. India.

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1 Volume 116 No , ISSN: (printed version); ISSN: (on-line version) url: A REVIEW ON CLASSIFICATION OF BREAST CANCER DETECTION USING COMBINATION OF THE FEATURE EXTRACTION MODELS ijpam.eu 1 K. Rajendra Prasad, 2 C. Raghavendra, 3 K Sai Saranya 1,2,3 Department of Computer Science & Engineering, Institute of Aeronautical Engineering. Hyderabad. India. 1 krprgm@gmail.com, 2 crg.svch@gmail.com, 3 sharanyakolli28@gmail.com Abstract: Supervised and non-supervised classification techniques are used for detection of breast cancer. Aim of the computer aided diagnosis (CAD) system and mammograms was to avoid the misclassification rates for early detection of disease. Classification of cancer signs by radiologist is not an easy task especially for distortions of small size it is not easy to extract the information using morphology operations. Common problems faced in image processing techniques were Region of interest segmentation and classification. For classification task there are many existing classifiers such as support vector machine, neural networks etc. This paper presents a popular support vector machine for mammography classification and describes about key observations for efficient breast cancer detection. Keywords- Mammogram classification, texture features, GLCM, LBP, SVM, Mammograms. 1. Introduction Cancer is a major public health problem observed today[1]. In particular among women breast cancer is the most commonly found compared to men. When compared to other cancers breast cancer has high rate of death mortality[2].breast cancer which emerged as a leading cause of death in women ranks second to lung cancer [3]. and men diagnosed with cancer. In many cases it is not an easy task to diagnose cancer accurately. There exist many difficulties in detecting breast cancer because of architectural distortion, asymmetry structures and breast distortion patterns. In case of diagnosing second major difficulty was due to presence of noise patterns or even due to uneven distribution of milk ducts in the breast region. Hence deducted results should have more increased accuracy along with reduced false positive rates. Mammograms are the commonly used method for detection of breast cancer by radiologist[4]. Mammogram images can be classified in to two types based on their tissue type such as fatty glandular and dense type. Depending upon the type of abnormality arise it can be classified as benign or malignant. Initially sample images used for this work were taken from mini Mammogram image analysis society (MIAS) database [5]. In an image textual property plays an important role in carrying out useful information along with image analysis [6]and for identifying image into benign or malignant [7]In breast cancer detection feature extraction was the initial step. There exists number of texture feature extraction methods. In this work three methods local binary patterns (LBP)[8], Gabor[9], Grey level cooccurence matrix (GLCM) were mentioned. Using different machine learning methods such as neural networks, support vector machines (SVM) accuracy percentage can be measured on a set of mammogram images. Among them support vector machines is an optimal choice for learning of mammogram data which works effectively on mammogram classification. Implementation work of feature extraction models mentioned in this work will be done by using MATLAB. Next to mammogram computer aided diagnosis (CAD) can be defined as a best optimal method for detection of breast cancer [10]. Initial stage involved in breast cancer detection were preprocessing step that involves segmentation of a breast region on a image after finding region of interest and false positive reduction stage [11] in order to diagnose breast cancer accurately. The most important feature used for comparing the performances of these feature extraction methods were by using of receiver operating characteristics which can be plotted by considering true positives and false rates. The Computer Aided Diagnosis (CAD) system a proved technique was been capable to reduce the error rate done by radiologist in order to avoid second opinion. Inputs provided for CAD in this work were considered from mini mammogram image analysis society (MIAS) database which contains both normal and cancer patient data. 2. Model for Masses Classification Hevine H.Eltonsy[12] proposed a novel morphology model in this model images from database were tested and trained by using multiple concentric layer model.. By utilizing computer aided diagnosis tool by radiologists to detect abnormalities from screening reports accuracy and sensitivity rates can be improved better. It was proven fact that the breast mass contains more number of invisible tumors cells. Efficient models should be 203

2 proposed to make detection of masses through computer aided systems. Automatic detection of masses in mammogram sevolves as an increasing interest in medical research fields. Common thing observed across existing works were segmenting mass region and extracting the textual features based on region of interest which may differ in their shape and size. Improving of irregular mass area around breast tissue disturbs the structure forming the multiple areas around the gland In this multiple concentric layer algoritherm among the multiple layers first seen depth layer or focal layer was considered and remaining layers near to focal area will be ignored here morphology activity on the focal area contains relevant information regarding presence of abnormal masses.here the central pixel point in the focal point layer can be kept as the seed point this seed point will be tested within each concentric layer by moving from boundary tothe center increasing pixel intensities. This model will be performed in three steps Analysis of data from the report of screening mammograms may not be an easy step because of characteristics of physical properties. In multiple concentric layer analysis breast segmentation method was performed by inbuilt method within computer aided system through which hidden dark background area gets separated from the extracted image histogram analysis can be performed after preprocessing stage image granulation will be performed in which here undistributed pixels will be removed. This granulation step reduces the size of the data without losing the information. In this step group of pixels in the segmented area which are connected to the highest granule part are labeled as the higher pixel intensity values. In this method pixel gets connected to the same granule level by connectivity procedure in which every pixel will be visited and it will be compared with the neighboring pixel if they are connected strongly to same granule level the procedure repeats until every pixel was visited in a segmented area. After granulation step morphology operation were performed on the breast region in which here unconnected pixel granule level gets eliminated because they don t contain any masses or lumps. Now the strongly connected pixels of same granule level referred as the focal area. Here concentric layers of an image can be measured by calculating distance from central pixel point. By comparing the distances from the focal point the nearest seed detected will be kept in equal granule level which will be considered as the brightest point. Images for this work were considered from the digital database for screening mammograms[ddsm]. 3. Classification of normal and abnormal patterns R.Nityaet.al[13] Proposes about classifications of normal and abnormal images from digital mammograms. Method used in this work was Grey level co_occurence matrix (GLCM) classical method for classification of patterns in an image. In This GLCM method features were modeled as the grey level two dimensional matrix. Statistical features analyzed in this GLCM method have been used successfully for segmentation of an image. This method is accurate in breast cancer detection but one disadvantage was that here by using GLCM matrix small invisible pixel elements cannot be extracted from the mammogram image particularly considering region of interest (ROI). Even though GLCM is an old extraction method but now days GLCM methodwas used along with the combination of other models.. Classification work in this model was performed by using a neural network classifier based on statistical measures. Neural networks contains x inputs y hidden units and one output unit. Here extracted features from the numerical parameters were considered as inputs to these neural networks which have connected set of input and output units for every assigned weight. Depending upon the accuracy of training data here classification method can be performed approximately. Five statistical features calculated using this model were correlation, energy, homogeneity, sum of square variance and entropy. 4. SVMN for Texture Classification M.Venkatramana et.al[14] In this review paper it had mentioned about recently emerging methods such as Gabor filters and wavelets. Texture classification is of many types but statistical based, structural based are gaining more popularity these days. GLCM can also be applied in colored cooccurence matrix. Applications of these Gabor filters and wavelets were observed in signal processing methods than structure based methods but in case of texture classification other older feature extraction methods such as GLCM can have increased accuracy levels when used in combinations with hybrid models. For classification task in this work 3 models were mentioned for nearest neighbors method distance is calculated from training examples and nearest neighbor selectedwould be based upon distance factor. Major disadvantage for nearest neighbor classifier was not being capable to handle the too many number of training examples. Next classifier method mentioned in this work was artificial neural networks but now days this classifier was recognized frequently due to support vector machines classifier. In neural network classifier during training stage calculated weights were assigned to the inputs layer and the hidden layer using back propagation 204

3 method. Next important classifier proposed were Support vector machine classifier a supervised learning classifier which replaces many traditional classifiers main task of this SVM classifier is to assign the likelihood items to assigned class by increasing the margin between the similar and dissimilar classes the classes which closely belongs to the margin are called as support vectors. Support vector machine classifiers were used both for binary classification and for multi class classification purpose. In case of nearest neighbors all training samples were selected but in case of SVM classifier only selected training samples which are required for classification purpose were used. classification in neural network classifier were based on the calculation of weights but in case of Bayesian rule classifier it follows probability theory both neural networks and Bayesian classifier are time consuming compared to the nearest neighbors and SVM based classifiers. These three mentioned classifiers were used in feature extraction models. Fig. 1.Shows the support vectors classification for set of mammogram images Figure 1. Support vectors for Set of Mammogram Images Anupa Maria sabu[15]cancer can be detected by using many textural features methods. Textural extractions from image carry very useful information. Analyses of textures from image can be of two types like statistical orstructural. In statistical type measures mean, variance, deviation, correlation were considered Analysis of texture from image plays important role in both segmentation, classification of mammogramimage. GLCM method was commonly used for calculating statistical measures. By modeling GLCM as a twodimensional grey level matrixtextual features would be extracted. Thirteen textural features were extracted using the GLCM method GLCM method was only useful for textual analysis and it was not used for comparison between two textures further. Another important textual feature mentioned in this work was grey level run length matrix. This matrix determines the pixels in a particular movement having same intensity values it was mentioned that the features extracted using this run length matrix generates great accurate outputs. For the combination of structure based and statistical based analysis here local binary patterns [LBP] were used. LBP [1] codes for a pixel can be compared by thresh holding it with the neighboring pixel whenever all pixels in a mammogram image were labeled histogram will be computed. Fig. 2 illustrates the key steps on mammogram classification using feature extraction methods. 205

4 Figure 2. Steps for Texture Features based Mammogram Classification In feature extraction methods performance were compared based on accuracy measure and best extraction method should be selected. As discussed in literature work such as old methods like GLCM may classify the best detection rates when implemented along with the combination of other models using the statistical measures. It was known from literature work that from LBP method textures such as spatial features from local image part were extracted. From the literature work on review of texture analysis models proposes support vector machine as the most suitable and accurate mammogram classifier method. Texturee classification features used with Support vector machine gives the best accuracy result compared with other classifiers. Review work also mentions about limitations of Gabor filters in signal processing. 5. Conclusion Radiologists required the effective diagnosis methods and these areutilize to the purpose of early stages of breast cancer detection that saves women life. Texture feature models GLCM and LBP performs best for extraction of mammogram image features, in which features were trained using SVM for building of models separately for normal and abnormal mammogram. Automatic classification works best onmammogramm image which detects normal and abnormal images. Malignant cluster benign clusters will be having less number of connected pixels based on the different scale factors. Mammogram images with more number of masses are believed to have highest statistical measure mean when compared to normal images. References [1] FabioA.spanhol.,et.al., A dataset for breast cancer histopathological image classification,ieeetransactions ON BIOMEDICAL ENGINEERING,VOL. 63,NO. 7,July [2] shen-chuan Tai et.al., An Automatic Mass Detection in Mammograms Based on Complex Texture Features, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 18. NO 2, MARCH [3] Bikesh Kr. Singh. Et.al., mammographic image enhancement,classification and retrieval using colour,statistical and spectral analysis International journal of computer applications volume 27-No.1,August 2011 [4] L. Tabar et al., Mammography service screening and mortality in breast cancer patients: 20-year follow-up before and after introduction of screening, The Lancet, vol. 361, no. 9367, pp , [5] J. Suckling et al., The mammographic image analysis society digital mammogram database, in Proc. Excerpta Med. Int. Congr. Ser., 1994, pp [6] Dr.K.Revathy et al., Applying EM Algoritherm for Segmentation of Textured images,proceedings of the world congress on Engineering 2007 vol 1 WCE 2007,july 2-4,2007,London,U.K [7] R. Haralick et al., Textural features for image classification, IEEE Trans.Syst. Man Cybern., vol. SMC-3, no. 6, pp , Nov [8] Oliver, A., et al., False positive reduction in mammographic mass detection using local binary patterns, Medical Image Computing and Computer- 206

5 Assisted Intervention (MICCAI). Springer, Berlin, Heidelberg, (2007) [9] Mohamed abdel-nasser.,et.al.,improvement of mass detection in breast xray images using texture analysis methods,artificial intelligence research and development. [10] Ibrahim Mohamed Jaber Alamin et.al., Improved Framework for Breast Cancer Detection using Hybrid Feature Extraction Technique and FFNN International Journal of Advanced Research in Artificial Intelligence,Vol 5,No.8,2016 [11] Maria V.Sainz de Cea et.al., Estimating the Accuracy Level Among Induvidual Detections in Clustered Microcalcifications IEEE transaction on Medical Imagning:: DOI /TMI , IEEE. [12] nevine H.Eltonsy.,et.al., A concentric morphology model for the detection of masses in mammographyieeetransactions ON MEDICAL IMAGING, VOL. 26, NO. 06, JUNE [13] R. Nithya.,et,al.,Classification of Normal and Abnormal Patterns in Digital Mammograms for Diagnosis of Breast Cancer, International Journal of Computer Applications ( ) Volume 28 No.6, August [14] M.venkatramana.,et.al,review of recent texture classification:methods, IOSR journal of computer engineering volume 14, Issue 1 (Sep. - Oct. 2013), PP [15] Anupa Maria Sabu.,et.al,textural features based breast cancer detection:asurvey,journal of emerging trends in computing and information sciencesvol. 3, NO. 9 Sep, [16] R. Jeevidha,V. Sowmiya, K. Kiruthiga, R. Priya, Collabration Complexity Reducing Strategy In Cloud Computing, International Innovative Research Journal of Engineering and Technology, vol 02 no 04,pp ,

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