CLASSIFICATION MODEL FOR BREAST CANCER MAMMOGRAMS

: Machine learning has been the topic of interest in research related to early detection of breast cancer based on mammogram images. In this study, we compare the performance results from three (3) types of machine learning techniques: 1) Naïve Bayes (NB), 2) Neural Network (NN) and 3) Support Vector Machine (SVM) with 2000 digital mammogram images to choose the best technique that could model the relationship between the features extracted and the state of the breast (‘Normal’ or ‘Cancer’). Grey Level Co-occurrence Matrix (GLCM) which represents the two dimensions of the level variation gray in the image is used in the feature extraction process. Six (6) attributes consist of contrast, variance, standard deviation, kurtosis, mean and smoothness were computed as feature extracted and used as the inputs for the classification process. The data has been randomized and the experiment has been repeated for ten (10) times to check for the consistencies of the performance of all techniques. 70% of the data were used as the training data and another 30% used as testing


INTRODUCTION
Breast cancer screening which is commonly made by mammography is a limited series of x-rays taken of the breast.Radiologist sees for any anomalous signs or patterns on the mammogram that might be breast cancer.These signs normally show up on the mammogram before any lump may be thought at the breast.If there is anything odd on this mammogram, more tests get to be made.These tests may add another mammogram, imaging, or a biopsy.Whether testing mammograms will change the probability of women dying from breast cancer has been argued for decades [1].It is definitely not an ideal test.Some cancers are failed to be detected by the mammogram.This false-positive mammogram effects lead to more experimentation, which is time-consuming and may have unnecessary apprehension.Since mammographic X-ray images are 2D projections of a 3D object, localization of features identified within the breast volume is not trivial.Furthermore, mammograms represent highly deformed configurations of the breast due to compression, thus the tumor localization process relies on the expertise of the clinicians.Breast cancerrelated studies have long been conducted by researchers around the world.
In the last few decades, various information production and machine learning techniques have been produced for breast cancer discovery and categorization, which may be separated into three important phases: Preprocessing, feature extraction, and classification.To facilitate explanation and analysis, the preprocessing of mammography images helps improve the clarity of peripheral regions and level system, and various methods have been reported to help in the process.Several transform-based quality analysis techniques are used to change the image into a new structure using the spatial frequency properties of the component level variations.In fact, there are many studies related to early detection is possible to show whether the state of the breast to be 'Normal' or 'Abnormal'.Among the most common classifier used for classification and early detection of breast cancer is Neural Network (NN).It is a model made to imitate the function of learning that owned the human brain.As stated by [2], the Neural classifier developed able to perform well by successfully classifying cancer and non-cancer (normal) images with an accuracy of 97%.However, this study did not show any comparison between the developed NN classifier and other types of the classifier.With regard to a single technique, [3] mentioned that in his mapping study, found that Decision Trees (DT), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were those most frequently adopted to build ensemble classifiers.
Therefore, in this paper on cancer detection, the focus will be on the classification part, whereby the use of three classifiers namely Naive Bayes (NB), Neural Network (NN) and Support Vector Machines (SVM), to accurately classify the mammographic images of the breast as Normal or Cancer will be presented.The results of this research will be useful for tracking visible tumors between images, as well as to help radiologists in their tasks to detect subtle abnormalities in a mammogram.

METHODOLOGY
This research methodology uses a data mining approach to classify data.In this case, there is an important phase that must be done to get the best research results.There are four phases involved namely sample selection, image enhancement and feature extraction, model development, and evaluation.

Sample Selection
A total of two thousand (2000) 2d-digital mammogram images were collected from the Central Pertamina Hospital, Indonesia (1000 normal breast images and 1000 cancer images) as shown in Fig. 1.These 2d-digital mammogram images were then converted to 3d-digital mammogram images for better analysis.

Image Enhancement and Feature Extraction
In preprocessing phase, the possibility distribution algorithm [4] is used for image enhancement using logical approach fuzzy with 5 parameters, namely α, β1, γ, β2 and max as shown in Fig. 2. From the required parameters, α represents the minimum value of the distribution, γ represents the average value of the distribution and max represents the maximum value of the distribution.The fuzzy transformation function used to get the entire value is defined as follows: The purpose of using the possibility distribution algorithm in improving image quality is to lower the level of pixel grayness that has a grayish value between β1 and β2.The way to do this is to provide a new intensity value in pixels between β1 and γ, γ and β2 with the opposite board value to the mean γ.The fuzzy rule below is used to perform image contrast enhancement based on Fig. 2: Rule 4: If β2 ≤ ui ≤ max then P = 2((ui − γ)/(max − γ))2 where ui = f(x,y) is the intensity of the i-i pixel.Regulations that lower the level of pixel grayness that has a grayish value between β1 and β2 are represented by Eqs.(7) and (8).
The steps of possibility distribution algorithm can be explained as the following: Step 1: Initialize parameter data matrix set by image to be improved.From the image, the minimum, maximum and average (mean) grayish values were obtained.The value β1 is set with the result of (min + mean)/2.Whereas the value of β2 is set with the result of (mean + max)/2.
Step 2: Fuzzification for all pixels.In this step, fuzzy rules were applied (Eq.6, Eq. 7, Eq. 8 and Eq. 9) to get a new grayish value.
Step-3: Modification for all pixels were executed, the fuzzy value of the data obtained from the new grayish value is powered by two.
Step-4: Defuzzification for all pixels.The new contrast data derived from fuzzy data values is calculated and multiplied by the matrix of the initial image data.
In this phase, feature extraction also being done to extract the important variables to be used in the next step which is development of classification model.For feature extraction, GLCM (Grey Level Co-occurrence Matrix) is being used.GLCM is the two dimensions represent the level variation gray at the image.GLCM is one of the popular statistical methods of extracting textural feature from images.According to [5], by extracting the features of an image by GLCM approach, the image compression time can be greatly https://doi.org/10.31436/iiumej.v23i1.1825reduced in the process of converting RGB to Gray level image when compared to other discrete wavelet transform (DWT) techniques.In this study, measurement of the contrast, variance, standard deviation, kurtosis, mean, and smoothness are computed as features extracted and shall be used in the classification stage.

Development of Data-driven Classification Model
In this work, Matlab software is used for developing the classification model and analysis.The three (3) machine learning techniques investigated to produce a data-driven classification models, were Neural Network (NN), Support Vector Machine (SVM) and Naïve Bayes (NB). Figure 3 shows an example of the 'Breast Image Extracted Features -Breast State' modelling structure.The inputs identified for the classification model were contrast, variance, standard deviation, kurtosis, mean, and smoothness.These features were extracted using GLCM methods explained in the previous section [5].Whereas the output for this classification model is the state of the breast itself, whether it is 'Normal' or 'Cancer'.

Naïve Bayes (NB)
The Naive Bayesian classifier is based on Bayes Theorem assuming independence among predictors.The Naive Bayesian model is easy to build, without the estimation of complex iterative parameters that make it very useful for very large datasets.Despite its simplicity, the Naive Bayesian classification often works very well and is widely used because it often outperforms more sophisticated classification methods.
Bayes Theorem provides a way of calculating the probability of posterior, P (c | x), of P (c), P (x), and P (x | c).The basis of the Naïve Bayes theorem used is the Bayes formula as follows [6]: https://doi.org/10.31436/iiumej.v23i1.1825 The Bayes Naïve classification assumes that the influence of predictor value (x) on a particular class (c) does not depend on the values of other predictors.This assumption is called class conditional independence.

Neural Network (NN)
Neural network is a model created to mimic the learning functions of the human brain.In the Neural Network, neurons are grouped into layers, called neuron layers.Usually, each neuron of a layer is connected to all neurons in the back and front layers (except inputs and outputs).Information sent in a NN, propagated layers -per -layers ranging from inputs to outputs without or through one or more hidden layers.Depending on the algorithm used, information can also be propagated backwards (backpropagation).Figure 4 shows the Neural Network with three neuron layers.

Support Vector Machine (SVM)
Support Vector Machine (SVM) is one of the prediction techniques, both in the case of classification and regression [7].SVM has a basic principle of linear classifier that is a classification case that can be linearly separated, but nowadays SVM is also able to solve non-linear problems by adding kernel concepts to high-resolution workspaces.In this space, a hyperplane (hyperplane) will be searched to maximize the distance between data classes.SVM can actually work on linearly separated data only.When the data is nonlinear then, the way that can be used is to use the kernel on the data.Kernel can be interpreted as a function that remaps data in the original dimension space into more dimensional space as shown in Fig. 5.

Experimental Procedure
As shown in Fig. 3, the features extracted from 2000 mammogram breast images were experimented with different classifiers that are Neural Network (NN), Support Vector https://doi.org/10.31436/iiumej.v23i1.1825Machine (SVM) and Naïve Bayes (NB).The data has been randomized and the experiment has been repeated for ten (10) times to check for the consistencies of the performance of all techniques.70% of the data were used as the training data and another 30% used as testing data.Accuracy based on confusion matrix is used as the performance indicator, to show which technique can best model the classification of normal and abnormal state of the breast.

FINDINGS
In this section, findings from three (3) different classification models NB, NN and SVM) will be presented.The inputs identified for all models were the contrast, variance, standard deviation, kurtosis, mean, and smoothness based on the GLCM approach applied to 2000 3d-digital breast mammogram images reported in the previous section.The outputs for all models were the breast state: 'Normal' or 'Cancer', which represented by '1' and '2' in the modelling process respectively.The extracted features with the breast states were then randomized.70% of the data were used for training and another 30% were used as testing.The objective of this modelling is to mimic the behavior of the expert in identifying the state of the breast whether it is 'Normal' or 'Cancer' by training the model with the extracted features (contrast, variance, standard deviation, kurtosis, mean, and smoothness) from the 3d-digital mammogram images and the identified breast state.The following subsections report the findings on all the training and testing work conducted.

Naïve Bayes Model Training and Testing Results
The first classification model built in this study is using the NB.The scatterplots for training and testing of NB model are shown in Fig. 6(a) and (b).While the detail results for the accuracy of the model for each experiment in the training and testing are shown in Table 1 and Table 2.In Table 1, it can be seen, that out of 1400 sample data used for training (70% of the total sample data), there are average of 964 correct predictions and average of 436 incorrect predictions for training.Thus, the average accuracy for the training is about 68.83%.Whereas for the testing, out of 600 sample data used for testing (30% of the total sample data), there are average of 420 correct predictions and average of 180 incorrect predictions for the whole 10 experiments.Therefore, the average accuracy for the NB model testing is about 70.07%.

Neural Network (NN) Model Training and Testing Results
The next classification model built in this study is using the NN.  3 and 4, there are average of 1237 correct predictions and average of 163 incorrect predictions for training.Thus, the average accuracy for the training is about 88.38%.Whereas for the testing, out of 600 sample data used for testing (30% of the total sample data), there are average of 530 correct predictions and average of 70 incorrect predictions for the whole 10 experiments.Therefore, the average accuracy for the NN model testing is about 88.27%.

Support Vector Machine (SVM) Model Training and Testing Results
The final classification model developed in this study is using the SVM.The scatterplots result of SVM model for training and testing are shown in Fig. 8 (a) and (b), while Table 5 and 6 show the detail results for the accuracy of the model for each experiment during training and testing.
From Table 5, it was found that, the majority of the outputs were correctly predicted by the SVM model, with average of 1392 correct predictions and average of only 8 incorrect predictions for training.Thus, the average accuracy for the training is about 99.40%.Interestingly, Table 6 also shown that the SVM model is able to correctly predict the output of the testing sample data with average of 593 correct predictions as against the incorrect predictions of only 7.This has made the average percentage of the testing accuracy to be 98.76%, slightly lower than its training accuracy.

DISCUSSION
In this study, the 2d-digital mammography images were first transformed into 3d-digital mammography and GLCM approach is applied to these 3d-digital breast images to extract the contrast, variance, standard deviation, kurtosis, mean, and smoothness as features to be input to the models.The supervised learning technique is used to train the models that infer a function from input data with labeled breast state, 'Normal' or 'Cancer'.2000 digital mammogram images of the breast have been analyzed.
Naive Bayes, Neural Network, and Deep Learning are among the most common machine learning techniques used in cancer detection [2,[8][9][10][11].However, in this study, ten (10) experiments have been conducted using the three (3) machine learning techniques namely Naïve Bayes, Neural Network and Support Vector Machine and the accuracy of each model in providing correct prediction for each experiment was recorded as shown in Section 3.
The result after ten (10) experiments show that the Support Vector Machine (SVM) gives the most consistent results in predicting or classifying correctly the state of the breast as 'Normal' or 'Cancer', with an average accuracy of 99.40% in training and 98.76% in testing.The SVM classification model has outperformed NN and NB models in the study, and it shows that SVM is a good choice for determining the state of the breast at the early stage.As compared to the previously developed NN model reported by [2], the currently developed SVM model outperformed their NN model.Their study also not considering any other machine learning techniques but just focusing on NN model only.
However, these single classifier models can be further experiment and improved.For example, as reported by [3], the majority of research have reported positive results concerning the accuracy of ensemble classifiers as compared to the single classifiers.The following research have supported the given statement [12][13][14][15][16][17][18][19][20][21].
Apart from that, the performance index used can also be varied, not just focusing on the accuracy of the model but to consider other aspect like computational time for the training and testing to be done, as well as Precision-Recall curve to capture the exact capability of the classifiers investigated [22][23][24][25].
Transfer learning has also seen as the alternative to the conventional supervised learning for breast cancer detection and classification as reported by [3].Therefore, comparison of model performance developed based on the supervised learning with transfer learning would be interesting to be explored in the near future.
Besides testing procedure that has been implemented in this study, validation with expert (radiologist) is seen crucial.The expert validation is time consuming, but it is important to be done in order to further validate the reliability of the developed models.

CONCLUSION
Today, the need for machine learning is rising in the field of breast cancer research.It has been used to screen and differentiate normal breast with cancer.The process of diagnosis can be very complicated.That is why for decades, researchers have developed various machine learning algorithms and architectures that can screen and differentiate cancerous images from normal ones by training the algorithms with various features extracted from the image of the breast.In particular, breast cancer diagnosis is more important than ever because the classification results will affect the treatment and safety of the patients.It requires not only a high prediction of accuracy, but also a high reliability and robustness.
In this study, ten (10) experiments have been conducted using the three (3) machine learning techniques namely Naïve Bayes, Neural Network and Support Vector Machine, using 2000 2d-digital mammogram images that have been converted into 3d-images.The accuracy of each model in providing correct prediction based on the confusion matrix has been reported for each experiment.The SVM classification model has outperformed NN and NB models in the study, and it shows that SVM is a good choice for determining the state of the breast at the early stage.However, further improvements can be done to the classifier models in producing more accurate and reliable results, for example, by exploring the ensemble classifier by combining different algorithm from the same type of classifier or different type of classifier to leverage the strength of individual classifiers.
As part of the future work, these models can also be extended to further classify the stage of the breast cancer (Stage 0, 1, 2, 3 and 4) by using the laboratory results of the patients as well as the auto measured diameter of the detected abnormality extracted from the digital mammogram images.

Fig. 1 :
Fig. 1: Some of the mammogram of normal and cancer images.

Fig. 6 :
Fig. 6: Scatterplots for (a) Training of the NB Model, (b) Testing of the NB Model.(a)(b)

Figure 7 (
a) and (b) show the scatterplots for training and testing of NN model, while Table 3 and 4 show the detail results of the NN model accuracy for each experiment in training and testing.As seen in Table

Fig. 7 :
Fig. 7: Scatterplots for (a) Training of the NN Model.(b) Testing of the NN Model.

Table 1 :
NB model training results for 10 experiments.

Table 2 :
NB model testing results for 10 experiments.

Table 3 :
NN model training results for 10 experiments.

Table 4 :
NN model testing results for 10 experiments.

Table 5 :
SVM model training results for 10 experiments.

Table 6 :
SVM model testing results for 10 experiments.