A MODIFIED BINARY PSO BASED FEATURE SELECTION FOR AUTOMATIC LESION DETECTION IN MAMMOGRAMS

International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 2, April 2018
DOI:10.5121/ijcsit.2018.10204 39
A MODIFIED BINARY PSO BASED FEATURE
SELECTION FOR AUTOMATIC LESION
DETECTION IN MAMMOGRAMS
Sheba K.U1
, Gladston Raj S2
and Ramachandran D3
1
Department of Computer Applications, BPC College, Piravom
2
Department of Computer Science, Government College, Nedumangad
3
Professor and HOD, Department of Imageology, Regional Cancer Center,
Thiruvananthapuram
ABSTRACT
This paper presents an effective feature selection method that can be applied to build a computer aided
diagnosis system for breast cancer in order to discriminate between healthy, benign and malignant
parenchyma. Determining the optimal feature set from a large set of original features is an important pre-
processing step which removes irrelevant and redundant features and thus improves computational
efficiency, classification accuracy and also simplifies the classifier structure. A modified binary particle
swarm optimized feature selection method (MBPSO)has been proposed where k-Nearest Neighbour
algorithm with leave-one-out cross validation serves as the fitness function. Digital mammograms obtained
from Regional Cancer Centre, Thiruvananthapuram and the mammograms from web accessible mini-MIAS
database has been used as the dataset for this experiment. Region of interests from the mammograms are
automatically detected and segmented. A total of 117 shape, texture and histogram features are extracted
from the ROIs. Significant features are selected using the proposed feature selection method.Classification
is performed using feed forward artificial neural networks with back propagation learning. Receiver
operating characteristics (ROC) and confusion matrix are used to evaluate the performance. Experimental
results show that the modified binary PSO feature selection method not only obtains better classification
accuracy but also simplifies the classification process as compared to full set of features. The performance
of the modified BPSO is found to be at par with other widely used feature selection techniques.
KEYWORDS
Binary particle swarm optimization, Feed forward artificial neural networks, Feature selection, k-Nearest
Neighbour.
1. INTRODUCTION
Breast Cancer is the most common cancer affecting women across the world [1].In India there is a
rising incidence of breast cancer especially among young women. Around 48% of breast cancer
patients in India are below the age of 50 [2]. 60% of the breast cancer cases in India are
diagnosed at an advanced stage due to the lack of breast cancer awareness, lack of screening
facilities and due to incorrect diagnosis. This drastically affects survival rate and treatment
options [2].
Currently, the most widely accepted screening modality for breast cancer is mammography as it is
reliable and economical [3]. Space occupying lesions are the most common symptoms of breast
cancer in mammograms [4]. Space occupying lesions can be of three types- masses, asymmetrical

40
breast tissue and architectural distortion of the breasts [5]. All these lesions can be classified as
either benign or malignant depending on their shape, texture, density and grey level intensity
values. Hence, each mammogram requires detailed evaluation in order to differentiate healthy,
benign and malignant parenchyma. Chances are that malignancies in mammograms may go
undetected or can be diagnosed incorrectly due to the strenuous job of evaluation, poor quality of
mammograms and subtle nature of malignancies [6]. A computer aided detection and diagnosis
system (CAD) for breast cancer can aid the radiologist in interpreting the mammograms and help
in the detection of suspicious lesions. They can provide a second opinion while the final decision
lies with the radiologist. Recent studies have shown that computer aided detection and diagnosis
systems have helped greatly in improving the radiologists’ accuracy in interpreting mammograms
[7].
The efficiency of a CAD system greatly depends on its accuracy, computational time and ease of
use. At present, the accuracy of CAD systems is not very high [8]. But, there is a need for near
perfection in lesion detection and diagnosis by the CAD systems. This is because false positive
rate can create undue anxiety and stress among patients whereas false negative rates can prevent
early detection of breast cancer which can cause serious threat to the patient’s life. Hence,
improving the accuracy of CAD system is very important as it can aid in improving the diagnostic
decisions.
CAD systems involve the following phases- Image acquisition, Image pre-processing, Image
segmentation, Feature extraction, Feature selection and Classification [9]. The performance of
CAD systems depend more on the optimization of feature subset selection than on the
classification methods. In mammograms, there is a large variation in the appearance of normal,
benign and malignant breast tissues with respect to their shape, texture and grey level intensity
values. Hence, it is necessary to extract texture, shape and grey level intensity features from the
ROIs. [10]. As a result, a large, diverse and complex feature set is obtained. Not all features are
required for classification as some of them are redundant, irrelevant, noisy and misleading and
can actually degrade the performance of the classifier. Also, if the training data set is small when
compared to the size of feature set, it can lead to the situation called curse of dimensionality [11].
This can also reduce the classifier performance. Due to these reasons, feature selection is
necessary to obtain optimal subset of features that can maximize classification accuracy and
reduce running time.
The aim of the paper is to develop an effective feature selection method that guarantees the
selection of optimal features which can greatly improve the performance of the classifier and
reduce its computational time.
2. OVERVIEW OF FEATURE SELECTION METHODS
Feature selection methods have been categorized into two types- filter and wrapper methods
[12].Filter method chooses an optimal subset of features by eliminating less significant features
using the statistical properties of the features. Wrapper approach incorporates learning algorithm
to select optimal subset of features. The wrapper approach usually outperforms the filter approach
in terms of classification accuracy as the former selects the optimal subset of features based onthe
performance of the feature subset when applied on a classification algorithm [13].
Due to limitations in conventional feature selection methods, recent research have employed
evolutionary computational techniques for feature selection. They include particle swarm
optimization (PSO), genetic algorithm, ant colony optimization etc. These techniqueshave been
extensively used in feature selection. PSO [14] has been used by many researchers for feature
selection as it has global searching ability, is easy to implement, converges quickly and takes less

41
computation time[15]. A number of recent studies have focused on PSO based feature selection
methods.
A. Unler et al. [13] in their paper have proposed a modified discrete PSO algorithm for feature
selection and compared it with tabu search and scatter search algorithms using publicly available
datasets. The algorithm was found to be competitive in terms of classification accuracy and
computational performance.
Xue et al.[16] in their paper developed a feature selection method based on modified
binaryparticle swarm optimization method. Decision tree classifier has been used for
classification.It has been compared with two traditional features selection methods by applying it
on 14 benchmarkproblems of varying difficulty. This method is found to achieve better
performance compared to the traditional feature selection methods.
A review of PSO algorithms and their variations are presented by the authors Tran et al. in their
paper [17]. Current issues and challenges for future research are also discussed.
In their paper [18], authors Yong et al. developed the barebones PSO to find optimal feature
subset where a reinforced strategy is designed to update the local leaders of particles in order to
avoid degradation of outstanding genes in particles. 1-NN is used as the classifier to evaluate the
performance and experiments show that the algorithm is competitive in terms of accuracy and
computational performance.
The authors Wong et al. [19] proposed an effective technique to classify regions of interest(ROIs)
of digitized mammograms into mass and normal breast tissue region by using PSO based feature
selection and SVM classifier. This method was successful in finding significant features that
greatly improved the classification accuracy of SVM.
In paper [20], the authors have proposed a modified PSO based feature selection for classification
of lung CT images. The experimental results shows higher classification accuracy compared to
basic PSO feature selection method.
The authors Zyoutet al. in their paper [21] have used PSO-kNN to select relevant GLCM features
for classification of microcalcification clusters in digital mammograms. They have obtained a
class accuracy of 88% which reveals that feature selection using PSO-kNN is effective.
Though extensive research has been done using PSO based feature selection in the classification
of microcalcifications in mammograms, it is found that not much research using PSO based
feature selection has been done in digital mammograms for classification of lesions as benign or
malignant [22].
In this paper, we propose a modified binary particle swarm optimization (MBPSO) algorithm for
selection of optimal feature subset for classification of mammograms as healthy, benign or
malignant. The modified binary particle swarm optimization introduced in this paper belongs to
the wrapper approach category. The modified version of BPSO differs from the traditional BPSO
in two aspects. They are:
1. In the traditional BPSO, velocity update is calculated from the previous velocity using
two best values- local best(lbest) and global best (gbest).In the modified version of BPSO
presented in this paper, besides lbest and gbest, iteration best(Itbest) is also considered for
velocity update .It is the best position obtained among particles in each iteration.

42
2. Secondly, in case the best fitness value is shared by two or more particles, the positions
of particle containing less number of features will be taken as the best position for lbest,
gbest and Itbest.
k-Nearest Neighbor (KNN) with leave-one-out method is used as the fitness function to choose
the optimal feature subset in MBPSO. Feed forward artificial neural networks (FFANN) with
back propagation has been used as the classifier. FFANN is trained using the optimal feature
subset. After necessary accuracy is obtained, the weights are frozen. Test data is then fed to the
FFANN and classification accuracy is measured.
The rest of the paper is organized as follows. Section 3 presents the traditional PSO and BPSO
method. The proposed methodology is described in section 4. Section 5 provides experimental
results and performance analysis. Section 6 concludes the paper.
3. BINARY PARTICLE SWARM OPTIMIZATION
Particle Swarm Optimization (PSO) is a population based search technique for finding optimal
solution in real number space modeled after the social behavior of bird flocks [23]. The concept
developed by Kennedy and Ebenhart consists of the following steps.
1. Initialize a set of random potential solutions called particles each of which are assigned
random position Xi and velocity Vi on D dimensions.
2. Evaluate the fitness function of each particle i in D dimensions. If the current fitness
value is better than the earlier fitness value obtained by the particle i, the local best value
lbesti of the particle i is updated to the current fitness value. The current location Xi= {xi1,
xi2… xiD} is assigned as the local best position lbi = {lbi1,lbi2……lbiD }.
3. Identify the particle with best fitness value achieved so far in the entire swarm. The
position of that particle is assigned as the global best position and is represented as G=
{g1, g2…. gD}. The best fitness value of that particle is assigned as the global best value
gbest.
4. The position Xi and the velocity Vi of each particle is updated using the following
equation.
Vi=ω Vi+c1* r1* (lbi-Xi)+c2*r2* (G-Xi) (1)
Xi=Xi + Vi (2)
where c1 and c2 are learning rates; r1 and r2 are random numbers in the range [0,1];ω is
the inertia weight.
5. Loop steps 2-4 until a good fitness value G is attained or a maximum number of iterations
are reached.
The original PSO was designed for real valued problems operating in continuous space. Since
many problems occur in discrete space, the original authors extended the real- valued version of
PSO to binary/ discrete space and named it as binary particle swarm optimization(BPSO) [24].
Since feature selection is based on discrete qualitative differentiation between variables, BPSO is
found to be more apt for feature selection [17]

43
There are two main differences between original PSO and BPSO. They are,
1. Particles in BPSO are represented as binary vectors i.e. as 0’s and 1’s. If xij=1, the feature
j of particle i has been selected, if xij=0, the feature is not selected.
2. In BPSO, the velocity is treated as probability vector which determines whether a binary
variable should take the value 0 or 1. Velocity is calculated in the same manner as in
PSO. It is converted to probability vector in the range (0, 1) using a sigmoid function. It
is given as
Sij= vij
e−
+1
1
(3)
The position of the jth
bit in the ith
particle is updated using Sijas follows.
xij =


 <
otherwiseif
Sif ij
0
1 δ
(4)
where δ is a random number between 0 and 1.
4. MODIFIED BINARY PARTICLE SWARM OPTIMIZATION METHOD
During the initial implementation of traditional BPSO to obtain the optimal feature subset,it was
observed that the performance of the BPSO can be further improved if the following changes are
incorporated in the algorithm.
• It was observed that when a particle bit, its local best bit value and global best bit value
are all same, it can lead to a state where the probability to include or exclude the feature
is 0.5. In problems with large number of features, it can result in high diversification. To
overcome this problem,a modification was made to the equation for velocity updation by
including a new factor called Iteration best(Itbest). Itbest is the best position attained by
any particle in each iteration.
Vid=ω * )()()( 332211 iddiddididid xgrcxItrcxprcv −+−+−+ (5)
Here c3 is the learning rate for the best position in each iteration. r3 is a random number
uniformly distributed in [0,1].
• During the execution of the algorithm, it is found that the fitness value generated for a
particle in a particular iteration may be equal to its local best value or the global best
value or the Iteration best value. In such cases, numbers of features are also considered in
choosing the best solution. If the current fitness value calculated for particle i happens to
be equal to it its local best value, but the number of features used to calculate the current
fitness value is less than the number of features used to calculate the local best value, then
the current position of particle i is chosen as the local best position and the current fitness
value is updated as the local beat value.
i.e. | ix |<| ilbest |
Then( iDii lblblb ,...., 2,1 )=( iDii xxx ,...., 21 )

44
Similar is the case with gbest and Itbest.
It has been found that velocity bounds, inertia weights and learning rates have a direct effect on
the particle’s motion[25]. Hence, it is important to assign values to these three parameters in such
a manner that it can effectively control the diversification and intensification of the particle. The
values for these respective parameters have been set based on experimentation as well as the
settings used in the previous papers [26].
• The velocity bounds include the upper velocity bound ( maxV ) and lower velocity bound(
minV ). They define the maximum and minimum velocity values that any velocity ijV can
take i.e.
if ijV > maxV then ijV = maxV
if ijV < minV then ijV = minV
Here maxV =6 and minV =-6
• Inertia weightω is updated using the following expression
ω =
T
t)( minmax
max
ωω
ω
−
−
(6)
where ω max and ω min are the upper and lower bounds for inertia weight, t is the current
iteration and T is the total number of BPSO iterations Here ω max=0.995, ω
min=0.5,T=100.
• The learning rates 1c , 2c are set as 1.49618 and 3c as0.5
• The number of the particles or the solutions is initialized as 30 i.e. N=30. The position of
the particle is defined as the binary vector Xi={ ,1ix , iDii xxx ......., 32 } where D represents
the total number of features i.e. D=117. }1,0{∈ijx , 1 if the feature is selected, 0
otherwise. d represents the total number of optimal features. d is initialized as 20. i.e.
.dxj ij =∑ The initial velocity of any particle i is taken as zero.
4.1 k-Nearest Neighbor with Leave One Out Cross Validation (kNN-LOOCV)
The choice of fitness function for evaluating the quality of features selected is an important
decision in PSO based feature selection methods. One of the popular fitness functions is the
classification accuracy of the induced model. In the proposed methodology, k-nearest neighbor
classification accuracy using leave-one–out cross validation has been used as the fitness function.
This means that modified BPSO searches for the optimal feature subset and k-NN classifier
evaluates each feature subset based on its classification accuracy using leave-one-out cross
validation.
k-NN algorithm[27] is a simple and popular non parametric method which stores a training
dataset of instances and classifies a query instance based on the attributes and similarity measure
of the instance with that of the training set. The instance is assigned a class most common among
its k -closest neighbours in the training set as measured by the Euclidean distance. The accuracy
of the classification is measured using leave-one-out cross validation (LOOCV) [28]. Suppose
thetraining data set consists of n instances. At each iteration, LOOCV uses one instance from the
training data set as test data and the remaining n-1 instances as the training data. k -NN classifier

45
is applied to find the class of the test data. This procedure is repeated for all the remaining
instances. Accuracy of the classifier is calculated as the ratio of the total number of correctly
classified instances to that of the total number of instances.
4.2 Modified Binary Particle Swarm Optimization Algorithm
Input:Training data set.
Output: Selected feature subset G
1. Initialize ,5.0,995.0,6,6,5.0,49618.1 minmaxminmax321 ==−===== ωωVVccc
D=117, d=20,T=100, t=0, n=30.
2. Randomly generate n initial particles which are binary vectors of length D such that the
total number of binary ones in each vector is d. i.e. .dxj ij =∑
for i=1 to n;
for j=1 to D
ijx =1 or 0
next j
next i
3. Initialize the velocity of n particles as 0.
for i=1 to n;
for j=1 to D
ijv = 0
next j
next i
4. Calculate lbest of each particle, Itbest and gbest
4.1 for each particle i=1 to n.
• Calculate the fitness function using k-NN LOOCV
lbesti=K-NN-LOO(Xi).
• Update the position vector of lbest i with the position of the particle.
),....,( 2,1 iDii lblblb = ( iDii xxx ,...., 21 )
next i.
4.2 Update Itbest with the highest fitness value obtained among particles in the current
iteration.
4.3 Assign the position vector of the corresponding particle to that of the Itbest.
(It1, It2…… ItD)= ( iDii xxx ,...., 21 )
4.4 Update gbest with the highest fitness value obtained among all particles so far.
4.5 Assign the position vector of the corresponding particle to the position vector of gbest.
(gb1, gb2….gbD)= ( iDii xxx ,...., 21 )
5. Repeat while (t≤T || gbest <= 0.99)

46
t=t+1;
Update ω using equation(6).
Generate a random no δ between 0 and 1
5.1 for i=1 to n
for j=1 to D
• Calculate velocity vij using equation (5)
• Update xij using the sigmoid function given in equation(3) and (4).
next j.
next i.
5.2 Calculate the new fitness value for particle Xi using K-NN-LOO().
fitness (Xi)=K-NN-LOO(Xi)
5.3 Update lbesti if the following condition holds true.
If((fitness(Xi)>lbesti)║((fitness(Xi)=lbesti)&&(│Xi│< │lbesti│)))
lbesti= fitness(Xi)
(lbi1, lbi2 ….lbiD)= (xi1, xi2 ….xiD) //Update position vector
end if.
5.4 Assign Itbest with the best local best value obtained in the current iteration t.
If ((lbesti(t) > Itbest)║((lbesti(t) = Itbest)&&(│lbesti(t)│< │ltbest│)))
Itbest= lbesti
(It1, It2 ….ItD)= (xi1, xi2 ….xiD)
end if
5.5 Update gbest with the best local best value obtained so far.
If ((lbesti>gbest)║((lbesti= gbest)&&(│lbesti│< │gbest│)))
gbest= lbesti
(gb1, gb2 ….gbD)= (xi1, xi2 ….xiD)
end if
end Repeat
Return selected feature subset G where jϵG if gbj=1
End
Procedure K-NN-LOO(Xi)
Begin
1. for j= 1 to m // m is the number of objects in training set.
• Temporarily remove th
j object(O j ) from the training set .
• Euclidean distance between the th
j object (O j ) to all the remaining (m-1)objects
in the training set is found. To calculate the Euclidean distance only the features
corresponding to binary bit 1 in the position vector of particle Xi is considered.
fork=1 to D
If(xik =1)
Dist( jO , lO )= Sqrt( sum+( 2
)lkjk OO − ) lO ϵ training set.
end if
next k.
• Find the K nearest neighbors to jO which has the minimum Euclidean distance.

47
• The most common class among K neighbors is assigned to O j .
next j.
2. for j=1 to m
• If (Class(O j )= real class of (O j ))
correct = correct + 1;
end if.
next j.
3. fitness value= correct/|training set|.
return(fitness value)
end
5. EXPERIMENTAL RESULTS
This section describes the database used, the test methodology and the results obtained, and
comparison of the proposed feature selection method MBPSO with other existing techniques.
5.1 Database
In order to evaluate the performance of the modified BPSO for feature selection, digital
mammograms have been taken from 2 sources. 83 mammograms have been provided by the
Regional Cancer Center, Thiruvananthapuram. All images are from Hologic Selenia Dimensions
full field digital mammograms system installed at Regional Cancer Center. These images are in
DICOM format with a resolution of 4096×3328 pixels. They have a pixel size of 65µm and bit
depth of 12 bits. 32 mammograms are malignant and the remaining 51 are normal.
200 mammograms have been taken from mini-MIAS database [29] which is a web- accessible
international resource. All images are in portable gray map format(.pgm). They are digitized at a
partial resolution of 0.05 mm pixel size with 2 bit density resolution using SCANDIG-3. All have
been expertly diagnosed and positions of the abnormalities have been recorded. 127
mammograms are normal, 44 are benign and 29 of them are malignant. For this experiment, a
total of 283 mammograms are used of which 178 are normal, 44benign and 61 malignant.
5.2 Implementation Environment
The experiment is implemented on Windows 10 Pro 64-bit operating system using MATLAB
2015b 64-bit, with Matlab image processing tools and statistical tools. All experiments are
implemented on Intel Core x64-based Processor of 2.4 GHz CPU with 8GB RAM.
5.3 Test methodology
All the digital mammograms have been preprocessed, ROIs are automatically segmented and
features are extracted as shown in our previous work [30]. Image preprocessing is required to
enhance the breast profile and to remove artefacts, labels, noise that can appear accidentally in
mammograms. It is also required to remove the unrelated parts that may appear in mammograms
like pectoral muscles. Median filter, global thresholding, adaptive fuzzy logic based bi-histogram
equalization [31] has been used to remove labels, artefacts and to obtain controlled enhancement.
In order to remove pectoral muscles, bounding box of the image has been used. Suspicious space
occupying lesions are automatically segmented from the mammograms for further preprocessing.
Multithresholding based on Otsu’s method and morphological operations are used for
segmentation of the ROIs. Normal mammograms do not contain lesions. But, same procedure for

48
pre-processing and segmentation are applied to them as well and ROIs are extracted. Figures 1
and 2 show the pre-processing and segmentation results obtained when applied on two
mammograms. Shape, texture and grey level intensity values of the ROI play an important role in
differentiating them as healthy, benign or malignant [32]. Therefore, 6 grey level intensity
features (mean, variance, skewness, kurtosis, energy and entropy), 52 GLCM features (energy,
contrast, correlation, variance, homogeneity, entropy, sum average, sum entropy, sum variance,
difference variance, first correlation measure and second correlation measure in four directions
0o
,45o
,90o
,135o
)44GLRLM features (SRE,LRE,GLN,RP,RLN,LGRE, HGRE,SRLGE,SRHGE,
LRLGE and LRHGE in four directions 0o
,45o
,90o
, 135o
) and 15 shape features (area, perimeter,
eccentricity, equidiameter, compactness, Thinness Ratio, Circularity, elongatedness, dispersion,
Shape index, Euler number, SD of edge and mass, Max Radius and Min Radius) are extracted.
Hence, a total of 117 features are extracted from the ROIs. These features are taken as input for
the feature selection technique.
ROIs obtained are divided into 2 sets. One set is used as the training set and the other set as the
test set. Training set contains 227 ROIs (144 normal, 35 benign and 48 malignant).There are
56ROIs in the test set (34 normal, 9 benign and 13 malignant). Feature selection using MBPSOis
done using the training set only. This results in an optimal feature set of 6 features. Theoptimal
features obtained are used to train the classifier, using the training set. A feed forward artificial
neural network with back propagation (FFANN) [33] has been used as the classifier for the
classification phase. It consists of three layers- an input layer with number of neurons equal to
number of selected features, a hidden layer made up of neurons and an output layer with three
neurons each representing a target class- normal, benign and malignant. Initial weights and bias
are randomly selected for FFANN usually between -0.1 to 1.0 and -0.5 to 0.5. To propagate the
inputs forward, non-linear log sigmoid function is used as the activation function. A matrix of
size 6 × 227 is given as input to the input layer. Using this feature matrix, FFANN processes the
data by comparing the network prediction of each tuple with the actual known class label.
FFANN learns using gradient descent method in the backward direction to iteratively search for a
set of weights and bias to minimize the mean-squared distance between network’s class
prediction and the known target value of the tuples. After the necessary accuracy is obtained, the
weights are frozen. The test data is then fed to the FFANN. For each test mammogram, a column
vector is created where each element represents one of the optimal features for the respective
mammogram. The class is then decided by FFANN based on the training results.
(a) (b) (c) (d)
Figure1. (a) Mammogram image PAT0004 obtained from Regional Cancer Center, Thiruvananthapuram.
(b) PAT0004 with pectoral muscles removed. (c) ROI obtained after segmentation. (d) Marked malignant
portion.

49
(a) (b) (c) (d)
Figure 2. (a) Mammogram image PAT0014 obtained from Regional Cancer Center, Thiruvananthapuram
(b) PAT0014 with pectoral muscles removed (c) ROI obtained after segmentation (d) marked malignant
portion.
The MBPSO feature selection method is compared to three other feature selection methods-PCA,
RFE and CART. The three methods are briefly described below.
• Principle Component Analysis (PCA): PCA [34] is a linear transformation method to
compress the data by reducing the number of dimensions without loss of information. It
makes use of covariance matrix, Eigen vectors and Eigen values to find the components
and then forms the optimal feature subset based on the components that are chosen.
• Recursive feature elimination (RFE): RFE [35] is a wrapper feature selection method
which works by recursively removing weaker attributes and building a model based on
those attributes that remain. It uses a model accuracy to identify which attributes
contribute the most to predicting the target. The stability of RFE depends on the type of
model that is used for feature ranking. The model used here is support vector machine.
• Classification and Regression Tree (CART): CART [36] is a decision tree induction
algorithm which constructs a flow chart like attribute structure where each internal node
denotes a test attribute and each external node denotes a class prediction. Since at each
node, the algorithm choses the best attribute to partition the data into individual classes,
these attributes can be taken as significant features and they form the reduced subset of
features.
In order to maintain uniformity, the same training set is used by all feature selection methods to
find the optimal features. Using the optimal features, training dataset is used to train the FFANN.
The test data set is then fed into the classifier and classification accuracy is measured.
5.4 Experimental Studies
In this section, a series of experiments are carried out to evaluate the accuracy and efficiency of
the proposed method MBPSO. As mentioned before, the same training data set is used for all
feature selection methods to find the optimal features and the same test data is used to measure
the classification accuracy. Classification accuracy is defined as the total number of correctly
classified samples in the test data divided by the total number of test samples.
Each feature selection method is run 3 times and the optimal feature subset which provides the
best classification accuracy is chosen. In case, two feature subsets have the same accuracy, the
one with lesser number of features is chosen. Table 1 gives the comparison of various feature
selection methods based on their classification accuracy and computational time.

50
Table 1. Comparison of various feature selection methods based on their classification accuracy and
computational time.
Feature selection
methods
Number of optimal
features
Classification
accuracy
Computational time
All features 117 87.3 0.610460
PCA 6 93.6 0.571530
CART 9 96.5 0.582645
RFE 11 92.9 0.596442
Proposed method 6 97.2 0.583761
Figures 3 -7 demonstrate the classification performance of various feature selection methods
using ROC curve and all confusion matrices.Fig 3 represents all confusion matrix obtained when
no feature selection is used. Out of 178 normal cases, 44 benign cases and 61 malignant cases,
172 normal cases, 30 benign cases and 45 malignant cases have been correctly classified giving a
classification accuracy of 87.3%.Fig 4 shows that the classification accuracy obtained when PCA
is used as the feature selection method is 93.6%.Fig 5 and Fig 6 demonstrates that the
classification accuracy obtained is 96.5% and 92.9% respectively when CART and RFE are used
as feature selection methods.Fig 7 shows the classification accuracy obtained when the proposed
method MBPSO is used as the feature selection method. It obtains a classification accuracy of
97.2% as 177 normal,42 benign and 56 malignant cases have been correctly classified.
From Table 1 and Figure 7, it can be seen that MBPSO reduces the feature set from 117 to 6. The
optimal features include SD of the edge, entropy, LGRE(00
), contrast (900
), perimeter, SGLGE
(00
).It gives a classification accuracy of 97.2% which is better than the classification accuracy
without feature selection.
PCA though reduces the feature subset to 6, the classification accuracy obtained is93.6% which is
less when compared to CART and the proposed method. Studies have shown thatPCA is unable
to capture accurately nonlinear relationships which exists in the complex biological systems.This
may be the reason for the reduced accuracy. The advantage of PCA is that PCA takes lesser
computational time when compared to other feature selection methods.
CART provides an optimal subset of 9 features and results in a classification accuracy of 96.5%.
The 9 features include LGRE (1350
), HGRE(900
), LRLGE (900
), SD of Edge, LRLGE (00
), LRE
(00
), LGRE (900
), LGRE(00
), contrast (900
).The computational time is also almost similar to that
of MBPSO. But the advantage of MBPSO over CART is that it uses lesser number of features as
can be seen from Table 1, figure 7 and figure 5. MBPSO also attains a sensitivity of 91.8% and
specificity of 95.5% in classification whereas CART attains a sensitivity of 88.4% and specificity
of95.4% .This means MBPSO results in greater number of malignant and benign cases being
correctly classified as compared to CART. This is due to the fact that CART can take only one
attribute at a time to make the split (decision) and thus if the decision depends on several
variables, chances of error rate is higher.To differentiate between normal, benign and malignant
breast tissues, shape, texture and histogram features have to be considered simultaneously in
order to make correct diagnosis. As this is not possible in case of CART, this may have resulted
in lesser sensitivity and specificity.
SVM-RFE provides an optimal subset of 11 features. They include entropy, SD of edge,
homogeneity, LGRE (900
), LGRE(00
), contrast (900
), difference entropy (1350
), difference
variance (1350
), SRE (450
). However, RFE is computationally expensive as compared to MBPSO
and other feature selection methods. This is because SVM-RFE goes through each feature one by

51
one in order to remove weaker attributes and to build a model based on the optimal attributes. It
also does not take into account the correlation between features.
The experimental results prove the efficacy of the proposed method and also show that the
performance of the proposed method is at par with other popular feature selection methods.
Figure 3. Classification performance without feature selection
Figure4. Classification performance with PCA as feature selection method.

52
Figure 5 Classification performance with CART as feature selection method.
Figure 6. Classification performance with RFE as feature selection method.

53
Figure 7. Classification performance with the proposed method as feature selection method.
6. CONCLUSION
Feature selection is an important pre-processing tool in building an efficient classification model.
In this paper, a modified binary PSO-KNN method for feature selection has been proposed in
order to develop a classification model to distinguish between healthy, benign and malignant
parenchyma in mammograms. Experimental results show that the proposed method obtainsan
accuracy comparable to other popular feature selection methods. At the same time, it reduces
computational complexity and also demonstrates high efficiency which is atpar with other well-
known feature selection methods. In future, modified BPSO can be applied to problems in other
areas as well.
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