FUZZY MULTI-LAYER SVM CLASSIFICATION OF BREAST …...primarily based on content of picture. This...

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International Journal of Mechanical Engineering and Technology (IJMET)

Volume 9, Issue 8, August 2018, pp. 1281–1299, Article ID: IJMET_09_08_138

Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=8

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

FUZZY MULTI-LAYER SVM CLASSIFICATION

OF BREAST CANCER MAMMOGRAM IMAGES

Venketkumar Hariraj, Wan Khairunizam1and Vikneswaran, Zunaidi Ibrahim,

Shahriman AB, Zuradzman M. Razlan, Rajendran.T, Sathiyasheelan.R

School of Mechatronics, University Malaysia Perlis

ABSTRACT

A huge increase in health issues has set new challenges to clinical routine for

patient’s record about diagnosis, treatment and follow-up, with help of data & image

processing it is possible to assist or automate the radiologist for diagnosis. Detection

of breast cancer is done with mammogram, which are low dose x-ray images.

Mammogram image play a totally vast function in early detection of breast most

cancers. Usually photo texture analysis is used for clustering and classification

primarily based on content of picture. This paper concentrated on Fuzzy-Multi layer

SVM (FMSVM) classifier for evaluating the features extracted and to determine its

effects. The proposed FMSVM version indicates promising consequences when

compared with different classifiers used most generally within the literature and can

offer a destiny for more sophisticated statistical features based most cancers

prognostic models. The proposed method is evaluated on a set composed of images

extracted from Mini MIAS databases. The examination over the images show that the

proposed method is efficient and effective for detecting the malignant, benign and

normal tumors, as well the accuracy achieved is about 98%. The outcomes show the

promising factors of the proposed methodology along with the suggestions for the

future work.

Key words: Mammogram, Feature Extraction, Fuzzy Multi-layer SVM classifier,

MIAS.

Cite this Article: Venketkumar Hariraj, Wan Khairunizam1and Vikneswaran,

Zunaidi Ibrahim, Shahriman AB, Zuradzman M. Razlan, Rajendran.T,

Sathiyasheelan.R, Fuzzy Multi-Layer SVM Classification of Breast Cancer

Mammogram Images, International Journal of Mechanical Engineering and

Technology 9(8), 2018, pp. 1281–1299.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=8

1. INTRODUCTION

Breast cancer is commonly found in women and some men around the world. In the

pathological diagnosis of breast cancer [1] the suspect tissue biopsy or fine needle aspiration

is achieved by the pathologist visualization. Mammography detects breast cancer through the

identification of abnormalities such as masses and/or micro-calcifications. However, due to

Venketkumar Hariraj, Wan Khairunizam1and Vikneswaran, Zunaidi Ibrahim, Shahriman AB,

Zuradzman M. Razlan, Rajendran.T, Sathiyasheelan.R


the complexity of breast structure, low disease prevalence, and radiologist fatigue,

abnormalities are often ignored. As reported by Cancer Research 5,000 Malaysian women are

diagnosed with breast cancer every year, most of them aged between 30 and 60 years, where

nearly half of those affected are under 50-years of age. About 10%–25% [37] of abnormal

cases shown in mammography have been wrongly ignored by radiologists. One way to reduce

the false negative rate is to use double reading, which can improve the detection rate by 5%–

15%. However, double reading is too expensive and time consuming. Thus, processing the

breast image for cancer detection technology attracts researchers‟ and radiologists‟ attention

[2]-[3].

The primary aim of this work is to develop an automatic breast cancer grading method in

X-ray Mammogram images in order to assist pathologists in enhancing the operational

efficiency as well as improving diagnostic confidence. This work presents an automatic

method for breast cancer grading based on a combination of pixel, semantic-level of features.

Although these features are directly computed from images reflecting explicit attributes that

pathologists look for grading breast cancers, there is another category of feature generation

inspired by convolutional methods. K-means clustering is a segmentation method [4]-[5],

used to automated segment from the images. The features were extracted independently and

evaluated through Fuzzy Multi-layer support vector machine (FMSVM) classification [6]-

[7].In this research, investigation was carried out for classification of breast shape with

mammogram images [8]; with the first step is the use of Weiner filter to reduce background

removal and noise removal. Next, the process will continue with the image segmentation and

morphological operation, then characteristic of shape extraction with invariant moment. The

last step is classification with Fuzzy-Multi layer support vector machine. This research aims

to develop a method that is capable of classifying various features of breast lesions from

mammogram images as one of the malignancy parameters [9] of breast cancer.

The reminder of the work is composed as follows: Section II discusses some state-of-the-

art of the prevailing methods. Section III, explains the methodology and provides a detailed

flow diagram of the complete method. Section IV, demonstrates the results and discussions

based on the proposed method and show the comparison with the existing methods. Finally, in

section V depicts the conclusion on the work.

2. RELATED WORK

This chapter provides a survey on opinions of diverse techniques in preprocessing,

enhancement and segmentation of mammogram pix which brings the main outline of breast

most cancers. One of the fine regarded techniques for the detection of breast cancer disorder

is the mammography that could display the ailment disease occurrence accurately. This phase

discusses approximately special studies works that are implemented via distinctive authors,

with the focus on detecting the spot of most cancers tissues in mammogram images. However

the accuracy of disorder prediction using mammogram is tough in case of presence of micro

variation in densities throughout distinctive tissues within the mammogram image. From the

analysis of these research works, better prediction strategies and capabilities can be

recognized in detail that can then be applied for further research work implementation.

Priya Darshini Velusamy [49], presented the superior adaptive fuzzy C means set of rules

along with changed vicinity growing set of rules which offers the fine result for the images

which has greater information and comparatively higher than okay-way and regular Fuzzy C

means set of rules because in k-approach the information point specially belong to one cluster

center but in advanced AFCM the records factor has assigned membership that's specified to

each cluster center so that the facts factor will belongs to more than one cluster center. Also

the author calculated the vicinity, perimeter and diameter of the tumor with the intention to

Fuzzy Multi-Layer SVM Classification of Breast Cancer Mammogram Images


find the exact portion and the edges of the tumor. Based on these values the medical doctors

can conclude the stage of the tumor and they could start the remedy.

Uma Ojha [13], classify recurrent or non-recurrent cases of breast most cancers is a

research that is statistical in nature. Still this work may be linked to bio medical evidences. In

this paper, WPBC dataset is used for finding an efficient predictor algorithm to expect the

habitual or non-ordinary nature of ailment. This may assist Oncologists to differentiate a good

diagnosis (non-recurrent) from a awful one (recurrent) and can treat the sufferers greater

correctly. Eight famous records mining methods have been used, four from clustering

algorithms and four from class algorithms (SVM, C5.Zero, KNN and Naive Bayes). The

effects of those algorithms are truly mentioned on this paper with vital outcomes. The

classification algorithms, C5.Zero and SVM have shown eighty one% accuracy in classifying

the recurrence of the ailment. This is found to be best among all. On the opposite hand, EM

turned into located to be the maximum promising clustering algorithm with the accuracy of

sixty eight%.

Lipismita Panigrahi [50], proposed a way used the benefits of ok-method segmentation

based on pixel that help to overcome the power minimization hassle of energetic contour

approach. This work also compared three boundary detection set of rules ok-method, Otsu and

energetic contour technique. Though the active contour method has many blessings, however

it isn't always in a position to properly discover the boundary in many images. The k-means

algorithm has the functionality for detecting accurate seed and boundary, but now not in all

the instances.

Table 1 Comparison table on existing classifiers and its pros and cons

CLASSIFIER ADVANTAGES DISADVANTAGES

Linear classifiers [38] Simple and effective for linearly

separable data

Poor performance for non-linearly

separable data

ANNs [39] Robustness, no rule or explicit

expression is needed

Long training time, initial value

dependent

BNN [40] Useful when there is finite data Need to construct model and estimate

the associated parameters

Decision tree [41] Low complexity Accuracy depends fully on the design

of the tree

Template matching [42] No training process needed, new

data can directly added to the system

Requiring large size database

Naïve Bayes [43] Simple technique, need small

amount of training

Small change in training data cause

big change in model

k-NN [44] Sometimes it is robust with regard to

noisy training data

it does not learn anything from the

training data and simply uses the

training data itself for classification

SVM [45] High accuracy, work even if the data

is not linearly separable.

High complexity and extensive

memory requirements

ID3 algorithm [46] Detection rate is increase and space

consumption is reduced

Requires large searching time

Table I. depicts various classifier algorithms along with its advantages and disadvantages.

In order to overcome the disadvantages of the above mentioned classifiers, FMSVM was

designed and thus it shows better trade off compared to the existing classifiers.

D.Saraswathi [51], proposed a green function extraction method for evaluation of digital

mammogram is performed. Initially, every mammogram picture is decomposed with the aid

of wavelet and curvelet the use of multilevel decomposition and wrapping method one after

the other. Then the coefficients are extracted from each decomposition level. The extracted

curvelet coefficients are given to the aid vector device. The entire performance is measured




the usage of type accuracy fee. The experimental results show that curve let gives a higher

performance in comparison to wavelet. Then, the extracted curve let coefficients are too large

to be categorized. In order to reduce the time complexity and to pick out the distinguished

features, optimization set of rules the use of swarm intelligence has been proposed. The

proposed PSO set of rules achieves high classification accuracy than the present technique.

Table 2 Summary of Different Algorithms Used for Image Analysis [47]

Authors Algorithm Result Disadvantages

Jain et al.

Active Contour

Model (Both region

based and boundary

based) with General

Classifier Neural

Network (GCNN)

83.47%

accuracy

ACM fails in images

containing high

noise and excessive

overlapping nuclei

Tosun et al.

Marker-controlled

watershed

algorithm and Fast

radial symmetry

transform

99% accuracy High computational

complexity

Basavanhally et al.

Active Contour

model based on

Color Gradient with

Hierarchical

Normalized cut

89%

accuracy

False positive rate is

little high

Karsnas

Intuitive

Segmentation using

vectorial data

Not mentioned

The region keep

growing without

spatial constraints

Dundar et al.

Gaussian Mixture

Model based spatial

information and

Expectation

Maximization (EM)

Algorithm

Overall

accuracy of

87.9%

Requires parameter

optimization

Cancer is one of the dreadful diseases discovered in most of the living being. A Cancer

have a look at is one of the difficult regions for the research. Commonly it is the second one

main reason of demise all over the global. At gift, one of the maximum important reasons of

most cancers demise amongst center aged women is breast most cancers. Mammography is a

technique utilized by radiologists for early detection and prognosis of most cancers in breast

mammogram images. Digital mammogram has turned out to be the best method for untimely

breast most cancers detection.

Digital mammogram captures a digital mammogram image of the breast and accumulates

it in a computer. Mammogram photos are very noisy, low-comparison, blur and fuzzy and

hence the mammogram images are superior for accurate identification of breast most cancers.

Processing these mammogram photos require excessive computational competencies. Hence

this research aims on effective enhancement of mammogram images. In order to overcome

the disadvantages of the existing SVM techniques, the Fuzzy Multi-Layer SVM has been

proposed. It depicts better trade-off between the existing SVM classifier, where multi-level

examination of breast images shows better efficiency in contrast to the conventional methods.

3. PROPOSED METHODOLOGY

Initial detection of the cancerous region in mammogram helps in early diagnosis of a diseased

person which can reduce death possibilities. Methods developed for detection of the

malignant region in mammograms may not be able to provide results successfully. To



overcome this limitation, it is necessary to develop an approach which can segment malignant

regions properly. This inspired to work on the problem of breast cancer detection in

mammogram images which are able to segment cancerous region along with detection.

Consumption of time in execution is also important to provide good results in real time.

Having this factor in mind, develop an important and significant method which first detects

the cancerous region and then segment the area covered by malignant tissues. This paper

focuses on detecting the malignant tissues which represent higher intensity values compared

to background information and other regions of the breast.

Figure 1 Flow Chart for the Proposed FMSVM Method

However, in case of some normal dense tissues having similar intensities to tumor region,

it is necessary to detect tumor region excluding those regions successfully. Here, a method

has been proposed which includes detection followed by segmentation of mammogram

images based on simple image processing techniques which provide good results. Our method

consists of three main steps (1) detection (2) segmentation and (3) Classification. In the

detection phase, with Weiner filter and thresholding operation is applied on original input

image which outputs malignant region area. In segmentation phase, a tumor patch is found

using morphological closing operation and image gradient technique to find the region




boundary. In classification phase, the resultant shows the region boundary and detected

malignant tissues on the original input image.

The proposed research method attempts to extract and analyze the malignant tissues from

the digital mammogram images. In the proposed research method, raw digital mammogram

images are taken as input image which is analyzed in order to final abnormalities from the

mammogram images. The proposed research method perform breast cancer detection in three

stages namely preparation, pre-processing and statistical decision making. The processing

flow of the proposed research method is shown in figure 1. This work proposed a method

based on image segmentation techniques for breast mammogram segmentation and Fuzzy

based Multi-layer support vector machine have been used for classification. For classification,

different types of features have been extracted. Figure 2 shows the flow diagram of the

proposed work.

Figure 2 Flow Diagram of the Proposed Work

3.1. Pre-Processing

For an automated detection using image processing techniques, preprocessing is the first step.

Image preprocessing generally involves undesirable noise expulsion and corresponding

enhancement in the image. Preprocessing can be achieved through different morphological

operations like dilation and erosion, low pass filtering (median and averaging) thresholding.

[17]. Region of interest (ROI) can be obtained in preprocessing stage which will reduce the

processing time. Pre-processing is always the main part of any input mammogram images,

which reduces the computational cost and also the finds the ROI (region of interest). In breast

imaging pre-processing is very necessary because those parts which are not part of breast can

misguide the algorithm for classification [16]. This will reduce the performance of the

proposed method.

Background Removal

The foremost objective of pre-processing is the automatic cropping of breast region from the

mammograms (finding ROI). For this symmetry, images which were having breast on the

opposite side were flipped for nipple pointing to the right. Then the process of cropping is

performed. To remove information of the background like labels, and wedges in the images,

first, the gray-scale image is converted to a binary image by thresholding, and then

morphological operations are applied to the binary image to remove all unwanted small

objects. Variety of techniques and algorithms typically represent this general category as

a start line for edge detection, region labeling, and transformations of these techniques, region



labeling, and analyses [37], are comparatively straightforward algorithms that are used for

several years to isolate, measure, and establish potential regions.

Figure 2 (a) Image before noise removal, (b) Image after noise removal

The image is scrutinized until a foreground pixel is found as in figure 2. An unlabeled

foreground pixel is marked with a new label and its position is pushed on a stack. While the

stack is not empty the pixels on the stack are marked with the label and the neighboring pixels

are pushed on the stack. When the stack is empty the search continues for the next seed point

for the float-fill. A binary image input is given to this method. Stack method works for

labeling the image and after labeling the regions can be removed which are not part of breast.

And then cropping is performed to scale down the image, to reduce the computational

complexity. Figure 3 shows the result of pre-processing in mammograms.

3.2. Filtering

Noise Removal

In order to remove noise from the noisy breast image, a detail preserving wiener filter is used.

Noisy medical images need more robust noise removal algorithms to remove noise while

preserving the vital details such as edges and texture information [37]. Figure 3 depicts the

input image given for pre-processing, i.e., for noise removal, whereas figure 4 shows the

representation of noise filtered image.

Figure 3 Pre-processing of Input Image Figure 4 Noise Filtered Image

Many researchers [23, 24] have found Wiener filtering to be effective for the initial noise

removal stage for breast cancer detection. This kind of filtering is finest in terms of MSE or

mean square error for inverse filtering and noise smoothing. It can be thought of as a linear

estimation of the actual image. It is based on a stochastic framework. Equation (1) is known

as wiener filter. Wiener filter in Fourier domain is represented as follows:

W(F1, F2) =

(1)

Where,

Actual image = Sx(f1,f2)

Additive noise = Sn(f1,f2)




Blurring filter = H(f1,f2)

Where f1, f2 filters

Sn input to the filters

Sx control range of the original image

Wiener filter proposed by Norbert Wiener in 1949. The basic principle of wiener filter is

by minimizing the mean squared error between the ideal image and the restoration image. If is

the ideal image and the restoration image, the mean squared errors mention in Equation (2) as

below:

MSE = E[f(i,j)-f(i,j)2] (2)

The original image f(i,j), here refers to error between the input signals. The higher of MSE

value refers to the lower image quality. The constant value is used to make approximations to

noise.

3.3. Segmentation using K-means clustering

K-Means is a method that is fairly easy and rapid clustering information. The main principle

of this technique is to develop a k prototype/center of mass (centroid)/average (mean) of n-

dimensional data set. This technique requires that the value of k is already known in advance

(a priori). K-Means algorithm begins with the formation of initial prototype cluster. Then the

prototype cluster is improved iteratively to converge (no significant changes to the prototype

cluster). This change is measured using an objective function J which is generally defined as

the sum / average distance to the centroid of each group of data. K-Means algorithm can be

implemented to masses detection on digitized mammogram.

Figure 5 Colored Segmented Region Figure 6 Spotted Cancerous Region

Figure 5 and 6 elaborates the spotted cancerous region and the colored segmented region

specifies the strength of the breast tissue. Let us consider an image with resolution of x*y and

the image has to be cluster into k number of cluster. Let p(x, y) be an input pixels of the

cluster and ck be the cluster centers. The algorithm for k-means clustering is following as in:

1: Determine number of clusters.

2: Initiate each cluster‟s center randomly.

3: Calculate the distance between points and the centers.

4: Allocate each point to the nearest cluster.

5: Recalculate centers.

6: Calculate the distance between points and the centers.

7: If no point moved, stop. Otherwise, go back to step 4.

Consider „x‟ as a data point and Xk as the cluster centroid of cluster Ck. Then the objective

function is described as follows:

∑ ∑ (3)



where E is the sum of squared error for all data points (Na et al., 2010). Algorithm shows

the K-means algorithm process.

It partitions N data points to K disjoint subsets Sj where j=1, 2,..., K. It is based on

minimization of the objective function,

∑ ∑ (4)

where xn is a vector representing nth

data points in Sj and μj is the geometric centroid of

data points. The algorithm consists of two steps: choosing the number of cluster K, and then

implying the K-means clustering to the image.

Although k-means has the extraordinary gain of being easy to put into effect, it has a few

drawbacks. The quality of last clustering outcomes is relies upon on the arbitrary choice of

initial centroid. If the preliminary centroid is randomly chosen, it'll get extraordinary result for

exclusive initial centers. The initial center will be carefully chosen so that desire segmentation

has been obtained. And also computational complexity is another term which essentially to be

consider while designing the K-means clustering. It relies on the number of data elements,

number of clusters and number of iteration.

3.4. Feature Extraction

Most of the approaches proposed in the literature extract features from the entire region of

interest (ROI). Instead of extracting features from the entire ROI, our proposed approach

extracts features from the abnormal regions detected within the ROI. An abnormal region is a

subset of the ROI that shows a significant increase as compared to the neighboring areas.

The size of the detected abnormal region is normalized with respect to the ROI, when the

value is maximum. After segmenting the mass from the ROI, this work computes a set of

features that is related to the geometry and texture of the boundary and its neighbor regions. It

is known that a typical benign mass has a round, smooth, and well-circumscribed boundary,

whereas the boundary of a malignant tumor is usually speculated, rough, and blurry. Thus, a

boundary analysis can be used to classify the masses into benign or malignant. This work

investigates both the geometry features and the texture features [30]. Although these features

have been used in different publications, by combining them together in this paper to get

better performance. The tissue level features quantify the distribution of the cells across the

tissue; for that, it primarily makes use of either the spatial dependency of the cells or the gray

level dependency of the pixels. Based on these characteristics, some important shape and

morphological based features are explained as follows.

NOTE

Area (A): The nucleus area can be represented by nucleus region containing total number of

pixels; it is shown in

∑ ∑

(5)

Where, „A‟ is nucleus area and is segmented image of x rows and y columns.

Brightness: The average value of intensity of the pixels belonging to the nucleus region is

known as nucleus brightness.

Diameter: The largest circle‟s diameter circumscribing the nucleus region is known as

diameter; it is shown in

√ (6)

Where, x1,y1 and x2,y2 are end points on major axis.

Perimeter (P): The length of the perimeter of the nucleus region is represented using

P = Even Count +√ (count) unit (7)




Compactness: Compactness is the ratio of area and square of the perimeter. It is formulated

as

Compactness= Area/Perimeter2

(8)

Mean: Mean is the mean pixel value within the ROI. It is important to know the brightness of

ROI since the tumor has high value of brightness

Mean=∑ ] ]] (9)

Variance: The variation is a measure of the distance between two clusters (partitions of

elements). Clustering with clusters is represented by a random variable X, X = {1,…, k} such

that P i = |X i | /n, i X, and n = ∑i X i is the variation of information between two clusters X

and Y.

Thus V(X, Y) is represented using

V(X, Y) = H(X) = H(Y) − 2I(X, Y) (10)

where H(X) is entropy of X and I(X, Y) is mutual information between X and Y. V(X, Y)

measures how much the cluster assignment for an item in clustering X reduces the uncertainty

about the item's cluster in clustering Y.

Standard Deviation: Standard Deviation is the root mean square (RMS) deviation of the

values from their arithmetic mean. It is the most common measure of statistical dispersion,

measuring how widely spread the values are in a data set. If the data points are all close to the

mean, then the standard deviation is close to zero. If many data points are far from the mean,

then the standard deviation is far from zero. If all the data values are equal, then the standard

deviation is zero. Tumor is far from zero.

SD = ∑ (* [ ] ]]+ ) 2 (11)

Entropy: Entropy represent the measure of disorder in an object gray level organization,

large value of entropy correspond too much disorganized distribution, such as salt and paper

random field. Low entropy images have very little contrast. Image that is perfect flat will have

zero entropy. Tumor has negative low value of entropy.

Entropy = NH (Input Image [X_ROI[i], Y_ROI[i])* log NH (Input Image [X_ROI[i],

Y_ROI[i]) (12)

Correlation: Correlation is the most famous statistical approach. It is popular today, by virtue

of good performance. The Co-occurrence matrix contains elements that are counts of the

number of pixel pairs for specific brightness levels, when separated by some distance and at

some relative inclination [17]. Moreover, correlation produces a large value if an object

contains large connected subcomponents of constant gray level, and with large gray level

differences is created between adjacent components. So the tumor has high value of

correlation

Correlation = ∑ [ ] ]]] (13)

Where NH is normalized histogram vector and SD is Standard Deviation.

NOTE

3.5. Fuzzy Multi-Layer SVM Classifier

In this research, a Fuzzy Multi-layer SVM classification method has been proposed to detect

the breast cancer disease [18]. It is an extension of conventional or crisp set theory, which is a

collection of elements that have a number of common properties. Any element x in a crisp set

A is assigned a value of 0 or 1.



A fuzzy set, on the other hand, is unlike a crisp set. Each element x in the fuzzy set „A‟ in

X has a grade of membership fA(x), which is a real number in the interval [0, 1]. In fuzzy set

theory, the closer the value of fA(x) is to unity, the larger the grade of membership of x in A.

Fuzzy image processing includes techniques that are used to process and represent an

image‟s features and regions as fuzzy sets. Depending on both the problem to be solved and

the chosen fuzzy technique, image processing and representation are determined. Fuzzy image

processing is important for two main reasons. First, fuzzy set theory and fuzzy logic are

powerful tools to represent and process human knowledge as a series of fuzzy if-then rules.

Second, in image processing difficulties arise due to the randomness, ambiguity and

vagueness of data; and fuzzy techniques can manage them efficiently. Moreover, images by

nature possess a degree of fuzziness due to their borders are not always well defined.

Imperfection problems in image processing such as grayness ambiguity and geometrical

fuzziness are fuzzy as well. Examples like the questions of whether a certain pixel should

become brighter or darker than its current value, or where a particular object is located in a

scene analysis problem. The algorithm for Fuzzy Multi-layer SVM is given below:

Initialize output SVM

Initialize hidden layer SVM

Compute Kernal Matrix for hidden layer SVM

Train hidden layer SVM on perturbed dataset

repeat

Compute kernal matrix of output layer SVM

Train output layer SVM\Use feed forward to create training

Sets for hidden layer SVMs

Train hidden layer SVMs

Until maximum number of epochs reached.

Figure 7 Multi-layer feed forward propagation

FMSVM classifier has been used with correlation features to classify the breast cancer as

benign or malignant. Chen et al.[37] introduced a 25-input node multilayer feed forward

which consists of 24-dimensional image feature vector from the mammogram image, together

with a predefined threshold of the input layer to classify the cancer. The introduced system

has a relatively high accuracy in classifying malignancies and thus could help inexperienced

operators to diagnose mammogram images. One of the striking advantages of FMSVM is that

it could be further optimized by supplying larger set of mammogram images as the FMSVM

is well-trainable.




Figure 7 shows the Fuzzy multi-layer SVM has been proposed for regression problems. A

regression dataset: {(x1, y1), . . . ,(xℓ, yℓ)}, where xi are input vectors and yi are the scalar

target outputs. The multi-layer architecture contains an input layer of D inputs. Then, there are

a total of d SVMs Sa, each one learning to extract one latent variable f(x|θ)a from an input

pattern x. Here θ denotes the trainable parameters in the hidden-layer SVMs (which are the

support vector coefficients and the biases). Finally, there is the main Fuzzy Multi-layer

Support Vector Machine M that learns to approximate the target function using the extracted

feature vector as input. For computing the hidden-layer representation f(x|θ) of input vector x,

this works make use of:

∑ (α,* (α)- αi(α)) K1(xi, x) + bα (14)

which is iteratively used by each SVM Sa to compute the element f(x|θ)a. the target values

for hidden-layer features between -1 and 1, so if some target output is larger than 1 for a

feature simply set the target value to 1. To allow the hidden-layer SVMs to extract different

features, symmetry breaking is necessary. For this, this work randomly initializes the trainable

parameters in each hidden-layer SVM. However, a better way to initialize the hidden-layer

SVMs is to let them train on different perturbed versions of the target outputs. Therefore,

initially construct a dataset (xi , yi + γ a i ), with γ a i some random value [−γ, γ] for the

hidden-layer SVM Sa, where γ is another meta parameter.

Here, randomly selected training datasets are chosen for calcification, which are

confirmed by the doctors and others are randomly selected from the normal region. The result

of the malignant calcification is obtained from the results the Fuzzy multi-layer SVM. The

true positive ratio refers to the positive detection ratio which displays the multiple micro-

calcifications are detected. The performances of the malignant calcifications are based on the

size of the training sets. The performance of the Fuzzy multi-layer SVM shows robust

performs compared to the existing methods.

4. EXPERIMENTAL RESULTS AND DISCUSSION

4.1. Mini-Mammographic (MIAS) Database

This paper uses the Mini Mammographic (MIAS) breast cancer data set. This dataset is

available online, the dataset contains important risk factors. These factors usually are used to

diagnose the breast cancer in labs and give reliable results. The data set contains various cases

that are classified into two tumor classes; Benign or Malignant. The dataset contains nine

features that determine the tumor type. This database was created by Mammographic Image

Analysis Society (MIAS) and contains 322 digitized breast images in the resolution

1024x1024. It includes radiologist‟s “truth”-markings on the locations of any abnormalities

that may be present.

4.2. Performance Measures

Experiments are carried out in MATLAB on a machine with Intel Quad Core processor

@2.83GHz and 8GB RAM. As discussed earlier, mammogram patches are taken from the

IRMA database (the MIAS and DDSM datasets). All images from this database (density

wise), as given earlier in Table 1, are used. The size of each mammogram patch is 128 × 128.

A validation scheme is used here to divide the image database randomly into training and

testing sets. The performance of our system (and comparative systems) is evaluated by

standard metrics of sensitivity, specificity and accuracy.

Another statistical test to estimate accuracy is known as F-measure which is harmonic

mean of Precision and Recall. This criterion is considered as a weighted sum of these two

quantities and is the main criterion in the evaluation of the proposed system and systems



presented in other references in the field of automatic mitosis detection systems. According to

this best value for this criteria is 1 and the worst value would be 0.

The performance of FMSVM classifier is compared with the k-nearest neighbor classifier

(k-NN) and support vector machine (SVM) classifier. The proposed classification approach is

evaluated using classification classified rate which is the ratio of correctly classified images to

the total classified breast biopsy images, sensitivity (SN), specificity (SP)

Sensitivity =

(15)

Specificity =

(16)

Overall accuracy =

(17)

Figure 10 Graphical Representation of Specificity, Sensitivity and Precision

Table 2 Computation of Specificity, Sensitivity and Precision

Classifiers Specificity Sensitivity Precision

FMSVM 0.97 1 0.97

MLP 0.527 0.446 0.527

k-NN 0.772 0.6 0.46

SVM 0.96 0.89 0.96

The overall correct classification rate of FMSVM classifier for Non-malignant Vs

Malignant Breast histopathology image classification can be seen from Table III. It shows that

FMSVM give 97% classification accuracy for non-malignant image class and 52.7%, 46%

and 96% classification accuracy for MLP, KNN and SVM respectively in case of malignancy

image data set. The experiments lead to the conclusion that texture analysis of segmented

breast histology images achieves higher classification accuracy using proposed method and

outperforms over low resolution analysis methods.

Figure 11 Graphical Representation of TP-Rate, FP-Rate and Precision

0

0.2

0.4

0.6

0.8

1

1.2

Specificity Sensitivity Precision

k-NN

SVM

FMSVM

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

TP-RATE FP-RATE PRECISION F-Measure

FMSVM

NFS

MLP

SVM




Table 3 TP-Rate, FP-Rate, Precision and F-Measure for MIAS Database [32]

Classifier TP-RATE FP-RATE PRECISION F-Measure

FMSVM 97.00% 3.20% 98.50% 98.00%

NFS 95.70% 4.60% 95.80% 95.70%

MLP 84.20% 13.20% 84.20% 95.70%

SVM 87.50% 11.70% 87.40% 87.40%

The TP-Rate/recall, FP-Rate, precision and f-measure values of SVM are 87.5%, 11.7%,

87.4% and 87.4%. The TP-Rate/recall, FP-Rate, precision and f-measure of Fuzzy Multi-layer

SVM are 97.00%, 3.20%, 98.5% and 98%.The proposed FMSVM shows better tradeoff

between the prevailing methods.

Figure 12 Graphical Representation of TP, FP, TN and FN

Table 4 Computation of TP, FP, TN, FN [33]

Classifiers TP FP TN FN

FMSVM 60 2 58 7

k-NN 41 30 25 24

SVM 58 2 53 07

MLP 29 26 29 36

The analysis and evaluation of performance of the proposed system, there are some

specific accuracy measurement criteria known as Precision and Recall that are used in many

of biological systems which are focused on specific characteristics such as the number of

mitosis. These measurement criteria‟s are defined

Four different modes occur based on binary classification.

True Positive (TP) parameter is Number of truly detected breast cancer,

False Positive (FP) is number of non-mitosis that are misclassified as breast cancer,

False Negative (FN) is number of objects or undetected incorrectly and

True Negative (TN) stands for the number of truly detected non-breast cancer.

0

10

20

30

40

50

60

70

TP FP TN FN

FMSVM

k-NN

SVM

MLP



Figure 13 Graphical Representation of Normal and Abnormal Error Rate

Table 5 Normal/ Abnormal Error Rate

Classifiers Error Rate/Normal

Image

Error Rate/ Abnormal

Image

FMSVM 0.08 0.05

k-NN 0.26 0.45

SVM 0.12 0.07

LDA 0.26 0.41

MLP 0.34 0.51

From thetable V results FMSVM model achieved the better error rate of about 0.08 and

0.05 Normal/Abnormal values in comparison with the existing model respectively. The error

rate of normal images for k-NN, SVM, LDA and MLP are provided as 0.26, 0.12, 0.26, 0.34

respectively, whereas the error rate of abnormal images for 0.45, 0.07, 0.41, 0.51 respectively.

Table 6 Accuracy of Extracted Feature [48]

CLASSIFIERS FEATURES EXTRACTED ACCURACY

FMSVM Shape and texture 98%

SVM Shape and texture 80.0%

SVM Curvilinear, GLCM, Gabor, Multi-resolution

statistical features 91.4

SVM Shape Feature based DWT 88.75

SVM Contour let Features 82.1

SVM Gabor wavelets and directional features 84.37

SVM Shear let Moments, GLCM, statistical features Normal/Abnormal: 87.3

Benign / Malignant :92.5

The quantitative evaluation of FMSVM classifier shows that it outperforms other methods

and achieves 98% correct classification rate for both classes in test dataset. Whereas the SVM

and KNN achieve 87.3% and 92.5% classification rate for malignancy class as can be seen

from Table VI. The overall correct classification rate of FMSVM classifier for Malignant

Breast histopathology image classification can be seen. It shows that FMSVM give 98%

classification accuracy for malignant image class on the selected feature respectively in case

of malignancy image data set. The experiments lead to the conclusion that texture analysis of

segmented breast histology images achieves higher classification accuracy using proposed

method and outperforms over low resolution analysis methods.

0

0.1

0.2

0.3

0.4

0.5

0.6

FMSVM k-NN SVM LDA MLP

Error

Rate/Normal

Image

Error Rate/

Abnormal Rate




Figure 7 Percentage of Infection in the Total Pixels

The results obtained give support of anticipation with the findings and diagnosis by a

senior radiologist with early stage of clustering. The accuracy of detection has increased, with

comparison of Malignant Tumor results with 98.0% our proposed. Based on the pixel rate, the

abnormality encountered in the tumor was obtained as 20.70%.This research, addressed Fuzzy

multi-layer SVM to find a best breast cancer diagnosis model and classification accuracies

were compared. The proposed approach is focused on classification of mammogram images

as either normal or abnormal (benign or malignant) using various classifiers. The experiments

were conducted with mini-MIAS database images. From the results, it is came known that a

better classification accuracy 98%, achieved at various levels with direction for normal and

abnormal classification using Fuzzy multi-layer SVM classifier and the better classification

accuracy is achieved at various levels with direction to distinguish between benign and

malignant using Fuzzy multi-layer SVM classifier.

5. CONCLUSIONS

In this paper, an efficient Fuzzy Multi-layer SVM classifier method for analysis of digital

mammogram is carried out. This method has focused on two steps. Step1: extracting features

based on texture and Step2: extracting features based on the morphology and combination of

these effective features. According to the simulation results it can be seen that the accuracy is

improved greatly. The whole performance is measured using classification accuracy rate. The

proposed method has been tested on dataset provided leads to better results. The proposed

FMSVM shows 98% accuracy over the extracted feature than the prevailing k-NN, SVM,

MLP methods. In future studies, features provided in FMSVM along with the statistical

features will be applied to improve the results of classifying the mammogram images. By

selecting the appropriate features, accuracy can be modified further.

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