Breast Cancer Classification by Artificial Immune Algorithm based
Validity Interval Cells Selection
Rima Daoudi and Khalifa Djemal
IBISC Laboratory, Evry Val dEssonne University, 91020 Evry, France
Keywords:
Breast Cancer, Classification, Artificial Immune System, Validity Interval.
Abstract:
We present in this work an Artificial Immune System (AIS) algorithm for breast cancer classification and diag-
nosis. The main contribution is to select memory cells according to their belonging to a validity interval based
on average similarity of training cells. The behaviour of these created memory cells preserves the diversity
of original cancer learning class. All these operations allow to generate a set of memory cells with a global
representativeness of the database which enables breast cancer classification and recognition. Promising re-
sults have been obtained on both Wisconsin Diagnosis Breast Cancer Database (WDBC) and (DDSM) Digital
Database for Screening Mammography.
1 INTRODUCTION
Cancer is a disease in which cells become abnormal
and replicate forming more cells in an uncontrolled
way. Breast cancer begins in the tissues that makes
the breast, cancer cells may form a mass called a tu-
mor (Arora et al., 2008). Tumor can be benign or
malignant. The correct diagnosis of breast cancer has
become a major problem in the medical field since
this famous type of cancer stills threatening the life
of most women. Indeed, approximately one in ten
women is affected by this disease in her lifetime. Al-
though some risk reduction can be achieved through
prevention, strategies in this direction can not allow
the elimination of the majority of breast cancers that
occur in low-income and middle-income countries.
Early detection remains the main way to fight against
the disease, it improvesthe chances of survivalas well
as breast cancer outcome (Daoudi et al., 2015).
There is no doubt that the evaluation and decision-
making processes carried out by the experts are very
important factors. However, intelligent classification
algorithms also help physicians, particularly by mini-
mizing errors of not experienced practitioners.
In this context, computerized diagnostic systems
are increasingly used to directly reach the final di-
agnosis using artificial intelligence algorithms that
perform the role of classifiers such as Neural Net-
works (Hagan et al., 1996),(Marcano-Cede˜no et al.,
2011),(Neveset al., 2015), Genetic Algorithms (Yang
et al., 2013) or Support Vector Machines (Zemmal
et al., 2016),(Torrents-Barrena et al., 2015). In the
middle of the 90s, a new artificial intelligence ap-
proach inspired by immunology, has emerged, called
Artificial Immune Systems (AIS). Several concepts of
the immune response were extracted and applied as a
solution to real-world science and engineering prob-
lems (De Castro and Von Zuben, 2000).
Indeed, the Artificial Immune System (AIS) is
a distributed system that can perform classification,
recognition and learning tasks using extraction, com-
munication and memorization processes. Efforts
made in this research focus have contributed to the
emergence of several algorithms that can be classi-
fied into three large families as the natural mechanism
responsible for the implementation: negative selec-
tion (Dasgupta and Majumdar, 2002), clonal selec-
tion (De Castro and Von Zuben, 2000) and artificial
immune network (de Castro and Von Zuben, 2001)
algorithms.
The immune network theory was proposed by
Jerne in 1974 (Jerne, 1974). The hypothesis was that
the immune system maintains a network of intercon-
nected idiotypic antibodies to recognize an antigen.
Negative selection is a mechanism that protects the
body against the autoreactivelymphocytes. It uses the
ability of the immune system to detect unknown anti-
gens without affecting the self cells (Somayaji et al.,
1998). The clonal selection theory has been proposed
by Burnet in 1959 (Burnet et al., 1959). It explains
how an immune response is mounted as a model of
non-self antigen is recognized by the system. There
Daoudi, R. and Djemal, K.
Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection.
DOI: 10.5220/0006057202090216
In Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016) - Volume 1: ECTA, pages 209-216
ISBN: 978-989-758-201-1
Copyright
c
2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
209
are two processes: the recognition of the shape of the
antigen and selecting the antibody specific to it. The
idea is that only antibodies capable of recognizing the
antigen are activated for proliferation (cloning + mu-
tation).
Since 1959, there have been improvements of the
Burnet’s theory, especially on the way the antigens
are recognized. But the basic principles of clonal se-
lection and affinity maturation by hypermutation are
sufficient for the purposes of artificial clonal selection
algorithms.
In 2002, De Castro and Von Zuben (De Castro and
Von Zuben, 2000) proposed a clonal selection based
algorithm named CLONALG. The principle of this al-
gorithm is to build an initial memory cells population
and expose them to the antigens (training examples)
for a number generations to develop a population of
more specific memory cells to these antigens through
the cloning and mutation processes.
The main learning steps of CLONALG algorithm
are:
Generation of initial memory cells by a random
selection of training examples.
Memory cells evaluation and selection of the most
representative of the antigen.
Cloning, mutation and re-selection of the best mu-
tated clone.
Maintaining of diversity by the rejection of the
less good memory cells and their replacement by
randomly generated ones.
Compared to other clonal selection algorithms,
CLONALG has low complexity and requires fewer
parameters that can influence the classification accu-
racy (Brownlee, 2005),(Zhang, 2011). It has been
successfully applied to solve various complex prob-
lems, and offers a promising precision in the field of
pattern recognition.
Several studies have been published to improve
the potentially negative features in the learning of
CLONALG algorithm, including the exploration of
the information contained in the population of mu-
tated clones(White and Garrett, 2003), (Tasnim et al.,
2014), or the generalization of memory cells(Sharma
and Sharma, 2011). In (Daoudi et al., 2014), the work
is to improve two limitations observed on CLON-
ALG algorithm, the rst in the way it is initialized,
and the second in the selection of memory cells to be
cloned to avoid rejecting cells and their replacement
by randomly generated ones. The rejection step of the
less competent memory cells in CLONALG is used
to maintain diversity in the algorithm, but no check
is made that the added random cells are better than
those which are rejected. The improvements consist
Figure 1: Cells Clonal Selection Artificial Immune System
(CCS-AIS) Diagram (Daoudi et al., 2014).
in maintaining diversity by creating initial antibodies
(memory cells) and new memory cells from averaged
local subgroups. Diagram of CCS-AIS approach pro-
posed in (Daoudi et al., 2014) is given in figure 1. The
results significantly improves the accuracy of CLON-
ALG. Nevertheless, we found that averaging cells op-
erations do not adequately represent the true diversity
of all examples of the class to learn. Indeed, even if
the initial memory cells (antibodies) are not randomly
selected, they do not often guarantee the overallrepre-
sentativeness of the class. In this work, we propose a
method to validate these cells by using cells selection
validity interval.
The rest of the paper is composed as follows: in
the next section we present the databases that we used
to evaluate our approach. Section 3 details the differ-
ent steps of the proposed Validity Interval Cells Se-
lection Artificial Immune System (VICS-AIS) algo-
rithm. The results of application and the work’s con-
clusion are given in sections 4 and 5 respectively.
2 USED DATABASES
To evaluate our proposed algorithm, we chose to ap-
ply it on two different databases the most used in the
field of diagnosis of breast cancer: Wisconsin Diag-
nosis Breast Cancer Database (WDBC) and Digital
Database for Screening Mammography (DDSM).
2.1 Wisconsin Diagnosis Breast Cancer
Database (WDBC)
To evaluate our work, we chose to apply it on the Wis-
consin Diagnosis Breast Cancer Database (WDBC). It
was supported by Dr. William H Wolberg et al. (Wol-
berg and Mangasarian, 1990). WDBC consists of data
from 569 breast fine needle aspirate (FNA) cases con-
ECTA 2016 - 8th International Conference on Evolutionary Computation Theory and Applications
210
taining 32 descriptive features, where the two first
features correspond to a unique identification number
and the diagnosis status (benign or malignant). The
rest 30 features are computed from a digitized image
of a FNA of a breast mass by obtaining a small drop of
fluid from a breast tumor using a fine needle. With an
interactiveinterface, active contour models are initial-
ized near the boundariesof a set of different cells. The
customized snakes are deformed to the exact shape of
cells, ten features are computed for each cell and the
mean value, largest value and standard error of each
feature are computed for each image. The case dis-
tribution includes 357 cases of benign breast changes
and 212 cases of malignant breast cancer. The de-
scriptive features are recorded with four significant
digits including:
1. Radius; 2. Texture; 3. Perimeter; 4. Area;
5. Smoothness; 6. Compactness; 7. Concavity; 8.
Concave points; 9. Symmetry; 10. FracDim = fractal
dimension The features are recorded with four signif-
icant digits, and since they are measured in different
scales, the error function will be dominated by large-
scale variables. Thus, to eliminate the effect of differ-
ent levels, standardization is needed before learning.
In our work the WDBC database is normalized in
the range [0, 1] according to the following equation:
x
i
=
x
o
i
x
min
X
max
x
min
(1)
Where x
min
is the minimum of the data X for all
i, x
max
is the maximum of the data X for all i, x
o
i
is
the original i
th
data of data X, and x
i
the normalized
feature value.
2.2 Digital Database for Screening
Mammography (DDSM)
The digital database for screening mammography
(DDSM) was assembled by a group of researchers
from the University of South Florida and was com-
pleted in 1991(Heath and Bowyer, 2000). It com-
prises 2620 cases collected from the hospital, ”Mas-
sachusetts General Hospital” (MGH), the Univer-
sity ”Wake Forest University” (WFU) and the hospi-
tal ”Washington University of St. Louis School of
Medicine” (WUSTL). DDSM has been widely used
by the scientific community in the field of breast
cancer diagnosis; it has the advantage of using the
same standardized lexicon by the American Col-
lege of Radiology (ACR) in the BI-RADS (Breast
Imaging-Reporting And Data System). Different pa-
tient records were made as part of breast cancer
screening and were classified into three cases: normal
cases (no lesions) benign, and malignant cases. Each
file is composed of four views that contain the ex-
ternal oblique (MLO) and craniocaudal (CC) of each
breast. These files are also provided with data anno-
tations by radiologists. These annotations are used
to describe the various lesions present in the images
such as the number and type of anomalies (microcal-
cifications / masses), the biopsy result (Benin / ma-
lignant), the location of lesions, etc. In this work, we
only deal with the case of masses (not microcalcifica-
tions). Sub-database of DDSM was created consisting
of 242 masses: 128 benign and 114 malignant. These
examples will be partitioned (in the same way that the
WDBC database) into training and test examples.
The description of breast masses is a very im-
portant step, three new descriptors: the Skeleton
End Point (SEP), Protuberance selection (PS) and the
Spiculated Mass Descriptor (SMD) were proposed
in ((Sellami-Masmoudi et al., 2009), (Cheikhrouhou
et al., 2011) and (Kachouri et al., 2012)) respectively,
which were compared to 19 other features proposed
in the literature ((Daoudi et al., 2014)). In this work,
all of the 22 features are used to evaluate the perfor-
mance of the proposed VICS-AIS classifier.
3 PROPOSED AIS ALGORITHM
BASED VALIDITY INTERVAL
CELLS SELECTION (VICS-AIS)
We propose in this work a method for validating the
memory cells by using a validity interval for improv-
ing breast cancer recognition. This validity interval
is based on the standard deviation of average simi-
larities of all training cells, more particularly of the
relevant class. As we mentioned in section 1, the cre-
ated memory cells in (Daoudi et al., 2014) which are
used in classification do not often ensure a good rep-
resentation of all original cells of the different training
classes (benign and malignant). To improve the rep-
resentativeness of theses training cells, we propose to
determine a validity interval allowing the selection of
more efficient memory cells. By using a validity inter-
val we garantee a better deversity for the set of mem-
ory cells, and we have less identical cells that may
slow down the training process. This proposed solu-
tion is composed of three main steps. The first one
consist in determining the validity interval of selec-
tion, the second step concerns the selection of initial
memory cells using validity interval presented in the
first step. The last step is the global AIS training sys-
tem.
Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection
211
0 100 200 300
0
0.1
0.2
0.3
0.4
0.5
(a)
Similarities
OAC(Benign)
0 50 100 150 200
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
(b)
Similarities
OAC (Malignant)
Figure 2: Histogram of similarities between the overall av-
erage cell (OAC) and training examples of Benign class (a)
and Malignant class (b).
3.1 Validity Interval of Selection
From training cells, we calculate an overall average
cell (OAC), then we determine its similarity (Sim)
with all the examples of the training class. We calcu-
late the statistical characteristics: the mean, variance
and standard deviation.
Similarity is the measure of match between the
antigen (training example) and a memory cell. In our
work, and as the attribute values of the used databases
are real, it is determined using the Euclidean distance
(ED) calculated by:
ED(OAC, Training
Example) =
s
n
i=1
(x
i
y
i
)
2
(2)
with OAC = x
1
, .., x
n
and Training
Example =
y
,
.., y
n
, and n is the database dimension.
Sim(OAC, Training
Example) = 1 ED (3)
The average similarity of the class (Sim
moy) is
the average of similarities of OAC with all instances
of the class. Figure 2 shows an example of the sim-
ilarities between the overall average cell (OAC) and
training examples of each class (left: Benign,right:
Malignant) of WDBC database. The figure illustrates
the diversity of training examples of each class, which
we must select the right memory cells representative
of the same diversity.
Subsequently, we calculate the standard deviation
σ of the similarities of each class by the equations:
σ =
s
1
M
M
i=1
(x
i
¯x)
2
(4)
With ¯x = The average similarity of the class
(Sim
moy), and M: the total number of training data.
The Validity Interval (VI) of each training class is
determined by:
VI = Sim
moy± σ = [Sim moy σ, Sim moy+ σ]
(5)
This interval will be used to validate the selected
clones to reach all final memory cells pool.
3.2 Initial Memory Cells Selection using
Validity Interval (VI)
In this step, the creation of the initial memory cells for
each training class is performed in the same manner
as in (Daoudi et al., 2014), i.e. from averaged local
subgroups of training examples. The aim is to create
initial cells representing all the data to learn, instead
of randomly selecting cells not necessarily represen-
tative of the class.
After creating the initial memory cells
(MC
1
, .., MC
N
), we compute the average simi-
larity of each one with all the Training examples
(TE
1
, .., TE
M
).
with N: the number of initial memory cells
(NB
MC
i
).
Sim
moy(MC
i
) =
1
M
M
j=1
Sim(MC
i
, TE
j
) (6)
If Sim
moy(MC
i
) VI ; MC
i
is kept in the initial
memory cells set.
Otherwise, it will not be considered.
3.3 AIS Training System
In this step, the training of the artificial immune sys-
tem is made. Average cells are created and added to
the set of the final memory cell as has been described
in (Daoudi et al., 2014), but with an additional condi-
tion.
Indeed, the memory cells (medium or mutated
clones) will be added to the memory cells only if their
average similarities are in the validation interval (VI)
of the relevant class.
If Sim moy(Cell) VI ; add Cell to final memory
cells. ( Cell= Average cell or mutated clone).
Otherwise, reject Cell.
In this way we enable the generation of memory
cells with an overall representativeness of each class
to learn. General diagram of VICS-AIS proposed al-
gorithm is given in Figure 3
At the end of a defined number of iterations (fixed
by the user), we dispose of a set of global memory
cells for each training class. These cells will be used
in the test phase to determine the class of each sam-
ple to be classified. The application results of our ap-
proach for breast cancer diagnosis are presented in the
following section.
ECTA 2016 - 8th International Conference on Evolutionary Computation Theory and Applications
212
Figure 3: Diagram of VICS-AIS composed of Step I: Validity interval Creation for selection (VI), Step II: initial memory
cells selection using (VI) and Step III: AIS training system.
4 EXPERIMENTAL RESULTS
The overall results of this research are given in this
section. First we present the parameters used in eval-
uation, then all achieved tests are given and discussed
in subsequent sections.
4.1 Algorithm Parameters
We present in this section all of the parameters used
in our evaluations of the VICS-AIS approach on both
WDBC and DDSM databases.
4.1.1 Number of Initial Memory Cells
The initial memory cells population must be con-
sistent enough to properly represent the learning
database. At the same time, the number of these cells
should not be very great because it affects the speed of
the algorithm. The number of initial memory cells of
each class (NB
MC
i
) which is also the number of local
sub-groups is randomly determined by the equation:
NB
MC
i
= round(rand(
X
3
,
X
2
)) (7)
With X the total number of training examples of a
given class.
4.1.2 Number of Clones
The number of clones of each memory cell is calcu-
lated proportionally to its similarity value by the fol-
lowing equation:
NB
Clones
= round(β Sim) (8)
Where β is a cloning factor fixed by user. In our
work, β was set to 5 experimentally.
4.1.3 Range of Mutation
The mutation interval of each clone is inversely pro-
portional to its similarity value. I.e, the larger similar-
ity value is, the less the interval of change is wide.
Thus, the mutation value is randomly selected be-
tween [Sim-1,1-Sim]. Table 1 summarizes all the pa-
rameters used in the evaluation of the proposed VICS-
AIS approach:
Table 1: Used parameters in evaluation.
Parameter Value
Similarity 1- Euclidean distance
NB
Clones
β Sim
β (Cloning factor) 5
Mutation Rand(sim 1,1 sim)
NB
MC
i
rand(
X
3
,
X
2
)
k (Local subgroup size)
X
NB
MC
i
Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection
213
After presenting the parameters used for the eval-
uation of the approach that we proposed to improve
the clonal selection, we present in the next section
the application results of VICS-AIS (Validity Inter-
val Clonal Selection Artificial Immune System). The
algorithm VICS-AIS aims to enhance the diversity of
the CCS-AIS algorithm (Daoudi et al., 2014). Indeed,
to improve the overall representativeness of the train-
ing data and preserve good diversity in the algorithm,
we proposed to use a validity interval for each class
to learn based on the statistical characteristics of the
latter.
4.2 Classification Results
Based on the parameters set in the previous section,
this work uses four-fold cross validation to evaluate
the performance of the proposed approach. We shared
our databases into four equal parts, and used three
parts, for training and one for testing at each evalu-
ation. After 1,2 5 and 10 generations (iterations) of
the algorithm, the memory cells generated at the end
of training are used in evaluation. The average of 10
successive runs is taken as the end result of an eval-
uation. Tables 2 and 3 present the classification re-
sults obtained by the VICS-AIS method on WDBC
and DDSM databases are respectively, for the differ-
ent number of generations.
Table 2: Classification results on WDBC database.
Generations (%) Train ±σ (%) Test ±σ
1 96.96 ±0.85 94.18 ±1.32
2 97.80 ±0.30 94.75 ±1.05
5 98.55 ±0.32 95.48 ±0.90
10 98.95 ±0.16 97.58 ±0.22
Table 3: Classification results on DDSM database.
Generations (%) Train ±σ (%) Test ±σ
1 94.55 ±1.40 94.64 ±0.87
2 94.70 ±1.60 94.91 ±1.12
5 95.54 ±0.56 95.18 ±0.80
10 96.66 ±0.20 95.76 ±0.39
On WDBC database, proposed VICS-AIS algo-
rithm obtained 97.58% classification accuracy after
10 generations, compared to CCS-AIS which pro-
vided 96.80%. On DDSM database, the final classifi-
cation result after 10 generations of VICS-AIS algo-
rithm was 95.76%, while CCS-AIS algorithm reached
94.98%. We also noticed a rapid learning of the
VICS-AIS algorithm with respect to CCS-AIS, and
this is thanks to the good selection of appropriate
memory cells, and elimination of redundant or iden-
tical cells. So we can say that the proposed approach
has contributed to the improvement of CCS-AIS al-
gorithm.
Since the VICS-AIS algorithm is an improvement
CCS-AIS algorithm, we present in Figure 4 a compar-
ison between the two approaches in terms of average
similarities of both Benign and Malignant classes of
WDBC database (because it is more consistent than
the base DDSM). The average similarities of final
memory cells obtained by the two approaches are cal-
culated and compared with the average similarity of
the original cells of the database. we also present a
comparison between these values and between the va-
lidity intervals of each approach in table 4.
The validity intervals of final memory cells ob-
tained by CCS-AIS algorithm on both benign and ma-
lignant classes are narrower than those of the mem-
ory cells obtained by our approach (table4). From
figure 4, we can observe that average similarities of
final memory cells of VICS-AIS are nearest to the
average similarities of training WDBC database for
benign and malignant classes, unlike average simi-
larities of CCS-AIS memory cells which are smaller.
From these obtained results, we can say that the pro-
posed VICS-AIS method is effective. Indeed, the
introduction of validity interval in the algorithm al-
lowed a global representation of the diversity of train-
ing data, which is important to avoid local minima and
ensures a more accurate classification.
4.3 Comparative Study
In this section, we provide a comparative study be-
tween the approach we proposed, and some clonal se-
lection algorithms of the literature (including CCS-
AIS) that we have implemented. We applied each al-
gorithm on the two databases (WDBC and DDSM)
using the same parameters listed in table 1. The re-
sults of each application for 10 generations are listed
in table 5.
By comparing the results, it is easily noticeable
that the VICS-AIS algorithm has the best classifica-
tion rate on both WDBC and DDSM databases. An-
other important point which illustrates the effective-
ness of improvements in CCS-AIS algorithm is the
standard deviation (σ). Indeed, the VICS-AIS algo-
rithm has smaller values of σ, which means that it
is the most accurate approach. It was reduced by
0.3% on WDBC database and 0.39 % on the DDSM
database. Learning the 10 generations of VICS-AIS
is also the fastest among the approaches listed in Ta-
ble 5, thanks to the good selection of memory cells,
and elimination of repetitive cells due to the cloning
and the mutation operators.
ECTA 2016 - 8th International Conference on Evolutionary Computation Theory and Applications
214
Figure 4: Comparison between average similarities values of WDBC database and final memory cells of CCS-AIS algorithm
and our approach.
Table 4: Comparison of Validation Intervals and average similarities values of WDBC database with final memory cells of
CCs-AIS algorithm and our approach.
(%) VI (B) VI (M) Sim moy (B) Sim moy (M)
WDBC [0.08,0.19] [0.08,0.20] 0.13 0.14
CCS-AIS(Daoudi et al., 2014) [0.06,0.12] [0.06,0.13] 0.09 0.09
VICS-AIS [0.08,0.12] [0.08,0.14] 0.10 0.11
Table 5: Comparison of classification results.
Algorithm (%) WDBC (%) DDSM
CLONALG (De Castro and Von Zuben, 2002) 89.86± 4.45 85.15± 6.46
AIRS (Watkins et al., 2004) 90.30± 1.36 89.15± 1.88
CLONAX (Sharma and Sharma, 2011) 93.40± 2.23 92.25± 1.47
MF-AIS (Daoudi et al., 2013) 95.03± 0.50 94.91± 0.61
CCS-AIS (Daoudi et al., 2014) 96.80± 0.52 94.98± 0.78
VICS-AIS (This Study) 97.58± 0.22 95.76± 0.39
5 CONCLUSION
The objective of this work is to provide help to the
experts for a second opinion for breast cancer diagno-
sis. In this aim, we presented in this paper an artificial
immune algorithm based validity interval for memory
cells selection (VICS-AIS). The proposed approach
selects the memory cells according to their belonging
to a validity interval based on average similarity to en-
sure global representation of the diversity of the data
to learn. The main contributions that we brought and
that justify the improved results are :
Improvement of initialization, by creating specific
initial memory cells for each learning class, and
selection of the most representative among these
cells.
Creation of potential memory cell for cloning and
mutation.
Use of a validity interval of selection to improve
the overall representativeness and preserve good
diversity of learning data.
The obtained results on WDBC and DDSM
databases show a great performance of the classifier.
Based on this work, we can say that the introduction
of validity intervals in the training of AIS algorithms
is effective to properly represent the true diversity of
the training data, and guarantee good quality memory
cells.
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