AUTOMATIC BREAST CANCER DETECTION WITH OPTIMIZED ENSEMBLE OF CLASSIFIERS

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International Journal of Advanced Research in Engineering and Technology (IJARET)
Volume 11, Issue 11, November 2020, pp. 2545-2555 Article ID: IJARET_11_11_253
Available online at https://iaeme.com/Home/issue/IJARET?Volume=11&Issue=11
ISSN Print: 0976-6480 and ISSN Online: 0976-6499
DOI: https://doi.org/10.17605/OSF.IO/NKS
© IAEME Publication Scopus Indexed
AUTOMATIC BREAST CANCER DETECTION
WITH OPTIMIZED ENSEMBLE OF
CLASSIFIERS
Poornima H.N.1 and Ganga Holi 2
1,2
Department of Information Science and Engineering,
AMC College of Engineering, Bannerugatta Road, Bangalore, India
ABSTRACT
Many people are being affected by breast cancer. When numerous procedures are
used to diagnose breast cancer, such as clump thickness, cell size uniformity, cell shape
homogeneity, and so on, the end outcome might be challenging to get, even for medical
professionals. Therefore, an automatic breast cancer detection model is developed in
this research work. This research utilizes four key steps to construct an intelligent breast
cancer detection approach: "(a) pre-processing, (b) segmentation, (c) feature
extraction, and (d) classification". The provided input image is first pre-processed using
the median filtering approach and “Contrast Limited Adaptive Histogram Equalization
(CLAHE)”. Then, Chebyshev Distanced- Fuzzy C-Means Clustering (CD-FCM) is used
to segment the pre-processed image for ROI recognition. The Augumented Local Vector
Pattern (ALVP), Shape features, and “Gray-level Co-occurrence Matrix (GLCM)” are
then extracted from the recognized ROI regions. The Improved information gain is used
to choose the most optimum features from the retrieved features. Finally, an ensemble
classification approach is used to complete the classification process. The “CNN-GRU
[Gated Recurrent Units (GRU)-Convolutional Neural Networks (CNN)], Support
Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbours (KNN)” are
all included in this ensemble classification approach. With the specified relevant
characteristics, SVM, RF, and KNN are trained. The last decision maker is the CNN-
GRU, which is trained using the results of SVM, RF, and KNN. The weight function of
CNN-GRU is improved utilizing a newly created hybrid algorithm-Slimemould Updated
Wildbeast Optimization (SUWO) formulated by integrating the principles of both Slime
mould algorithm (SMA) and Wildebeest herd optimization (WHO), respectively, in
order to improve the detection accuracy of CNN-GRU. Finally, a comparative
evaluation is undergone to validate the efficiency of the projected model.
Key words: ALVP, AUWM, Breast Cancer, Classification, CD-FCM, optimized CNN-GRU.
Cite this Article: Poornima H.N. and Ganga Holi, Automatic Breast Cancer Detection with
Optimized Ensemble of Classifiers, International Journal of Advanced Research in Engineering
and Technology (IJARET), 11(11), 2020, pp. 2545-2555.
https://iaeme.com/Home/issue/IJARET?Volume=11&Issue=11

Automatic Breast Cancer Detection with Optimized Ensemble of Classifiers
1. INTRODUCTION
Breast cancer is a prevalent kind of tumor that targets women, and its incidence is now on the
upswing [1] [2]. BC has recently emerged as a prominent cause of mortality among females.
To lower the fatality rate, early recognition and adequate monitoring of the stages of BC are
recommended. BC is typically diagnosed based on the tumour cell's proliferation, shape, and
grade. Tumors are divided into two categories: benign and malignant [3]. The benign tumours
are non-spreading, but the malignant tumours are much more invasive and life-threatening.
Malignant tumours are more sophisticated and cancerous when contrasted to benign tumours
[19] [23]. Besides that, it's indeed extremely difficult for clinicians as well as professionals to
distinguish amongst benign and malignant tumours since existing medical instruments are
woefully inadequate [4] [5].
In the present predicament, BC screening technologies are divided into two categories:
“non-invasive and invasive” procedures. Invasive approaches include electromagnetism,
magnetic fields, ultrasonography, and radioactivity tracers. Mammography is indeed an
important tool for detecting cancer at an early stage, before medical symptoms occur [6] [7]
[8]. Invasive models include traditional “X-ray mammography, FFDM, Ultra-sonography,
Impedance Tomography, Nuclear Tomography, ductography, scinti-mammography, Magneto-
mammography, diffuse light imaging, and Laser Breast Scanner” [9]. Non-invasive procedures
never use ionogene radiation, which is why they are much more widely used. IR and MR are
non-invasive methods even though they are free from side effects and may be utilised at any
time [10] [20].
Early detection procedures could improve the survival percentage of BC patients. Early
diagnosis and treatment strategies for BC have advanced dramatically in recent years [11] [12].
Nowadays, X-ray mammography and magnetic resonance imaging are by far the most often
used techniques. Both of these strategies have drawbacks and implications. Ionizing radiation
from X-rays is extremely dangerous, and should not be exposed to individuals for lengthy
stretches of time [13] [21]. The Magnetic Resonance Imaging technology is quite costly,
however it may be used for a lot of tests [25]. Although mammography is less expensive, it is
hard to maintain uniformity as well as precision in BC assessment, and additional mistakes can
occur throughout the examination [14]. Certain machine learning algorithms under the
supervised learning task, such as SVM, KNN, and LSSVM, were presented to improve estimate
accuracy and minimise correlation inaccuracies [15] [16]. Depending on the dataset, these
machine learning models classify the collection of attributes into malignant and noncancerous
groups [17] [22]. These approaches had an unsatisfactory CR, and the procedure was both
difficult and time-consuming [24]. As a result, this study will build image processing-based
machine learning algorithms to identify malignant and non-cancerous pictures from
mammography breast images in order to overcome all of these disadvantages and provide an
effective BC classification model.
2. RELATED WORK
3D convolutional network for automated BC identification [1] from breast ultrasound images
will be available in 2020. By adopting a novel densely deep supervision approach with multi-
layer features, the detection sensitivity was increased. Furthermore, the "voxel-level adaptive
threshold" was proposed for predicting threshold loss during cancer and non-cancer
discrimination. Reduced false positives and greater sensitivities were achieved with the
suggested technique.
[2] proposed an end-to-end model for detecting BC using a FCN and “bidirectional long
short term memory”. With the use of FCN as an encoder, they were capable of extracting high-
level features. Furthermore, the flattening layer converted the FCN output into a one-

Poornima H.N. and Ganga Holi
dimensional sequence that was supplied into the Bi-LSTM input. As a consequence, when
compared with the previous work, the provided work was more accurate.
Mehranpour et al. [3] forecast a high-resolution conformal array for identifying tiny BCs.
Within the array, the unique cavity-backed LP-ASP antennas have been created. Deep
penetration imaging was possible using the suggested model. In addition, by employing the
HAS approach, artefacts such as skin reflection and mutual coupling were eliminated. They've
also used the ICA to remove the artefact responsiveness of the channel at a preliminary phase.
Man et al. [4] have projected DenseNet121-AnoGAN, as a novel approach for breast histo-
pathological image classification as either benign and malignant. The propose work has
encapsulated two major factors: unsupervised anomaly detection and DenseNet. The mislabeled
patches were labeled using the unsupervised anomaly that was designed with the generative
adversarial networks (AnoGAN). In addition, from the discriminative patches, the multi-
layered features were extracted using the DenseNet. The proposed work was tested using
BreaKHis dataset, and the outcomes had exhibited that the proposed work was much suitable
for coarse-grained high-resolution images.
Mask RCNN [5] to create a new BC prediction framework. The “mammographic image
analysis society digital mammography database (MIAS) and the Curated Breast Imaging Subset
of DDSM (Digital Database for Screening Mammography) (CBIS-DDSM)” were used to create
the dataset for this study. The suggested pre-processing technique consisted of two main
modules: 1) artefact and noise reduction, and 2) muscle removal. The retrieved breast area was
used in DeepLab V3 and TensorFlow's Mask RCNN publically accessible. They used the CNN
model to train each of these backbone structures. The average precision for the segmentation
job, on the other hand, is greater, which aids radiologists in breast mass categorization and
malignant area segmentation.
Murtaza et al. [6] presented “Biopsy Microscopic Image Cancer Network (BMIC Net)” in
2019, with the goal of establishing an accurate and computationally viable BC classification
model. The scientists used a DL and hierarchical classification technique to divide BC into eight
different subgroups. BreakHis, a publically accessible dataset, was divided into two sets:
training and testing. They performed data augmentation on the training set and retrieved result-
oriented features with the help of DL. They've also used feature reduction strategies to improve
classification performance and elicit the discriminative feature subset.
Wang et al. [7] proposed a new classification framework for BC categorization based on
histology pictures. The researchers utilized the multi-network feature extraction model with
pre-trained DCNNs. With the effective feature dimension reduction, the E-SVM was trained.
3. PROPOSED DETECTION MODEL
This research intends to develop an intelligent BC diagnosis approach by following four major
phases viz., “(a)Pre-processing, (b) Segmentation, (c) Feature Extraction and (d) classification”.
The architecture of the projected model is manifested in Fig.1.
Let the collected input mammogram breast image be denoted as input
img . The steps followed
in the projected model are depicted below:

Figure 1 Block Diagrm of proposed model
3.1. Pre-Processing via Median Filtering and Clahe
Initially, the input images input
img are fed as input to the median filtering [35] mechanism for
suppressing the unwanted distortions in input
img .
The median filtering a renowned order-statistic filters, due to its excellent performance in
alleviation certain specific noises like “Gaussian,” “random,” and “salt and pepper” noises. The
median filtering is accomplished by means of sliding a window across the input image input
img .
The filtered image is the median value in the input window acquire at the location of the centre
of the window. For input
img with pixels )
,
( B
A , the median filtering can be accomplished as per
Eq. (1).
)
,
( B
A
img pre
= ( )
 
T
k
j
k
B
j
A
img
med inp

−
− ,
, (1)
Segmentation
• Chebyshev Distanced- Fuzzy C-
Means Clustering (CD-FCM)
Feature
selection
with
Improved
information
gain
Input Image
(mammogram
)
Pre-processing
Median
filtering
CLAHE
Feature Extraction
Breast Cancer Classification
CNN-GRU
Benign
Malignant
Weight optimized via SUWO
Augumented
Local Vector
Pattern (ALVP)
Shape features Gray-level Co-
occurrence
Matrix (GLCM)
SVM
RF
KNN
ROI Non-ROI

The 2-D mask with size n
n is denoted as T and k
j, points to the pixels in the image. The
outcome from the median filtering is denoted as median
img . This median
img enters as input to the
CLAHE for enhancing its image quality.
CLAHE works on tiles, which are tiny areas of a picture rather than the complete image. To
remove the false borders, the surrounding tiles are blended using bilinear interpolation. The
contrast of photographs may be improved with this approach.
The CLAHE [36] is used to improve the contrast. By eliminating artefacts in homogenous
zones, the CLAHE is effective in overcoming the over-enhancement problem of histogram
equalization. CLHE divides the data into rectangular blocks of equal size and performs
histogram equalization on them. In general, the CLAHE pipeline follows five basic phases. The
remapping function corresponding to th
a grey level pixel is called )
(a
fremap in this case. The
maximal pixel value is marked as a
max in the block. CLAHE's pre-processed image is denoted
as pre
img , and it's the final pre-processed image. The enhanced FCM uses the pre-processed
pre
img as input to isolate the Region of Interest (ROI) from the rest.
The matrix denoting the various grouping distribution over the pixel brightness values i.e.
grey level in a image is referred as GLCM” [38]. The extracted GLCM feature from segment
Im is
denoted as GLCM
f .
3.2. Constructed Ensemble Technique
The ensemble model is constructed by means of combing several machine learning models in
order to construct one optimal predictive model. This helps in enhancing the classification
accuracy. In this research work, an ensemble model is constructed by combing the CNN-GRU,
SVM, RF and kNN, respectively. The CNN-GRU is the final decision maker, and it is trained
with the outcomes acquired from SVM ( )
SVM
out , RF( )
RF
out and KNN( )
KNN
out is pointed as out =
( )
SVM
out +( )
RF
out + ( )
KNN
out . In order to enhance the detection accuracy of CNN-GRU, the weight
function of CNN-GRU is optimized using a newly developed hybrid algorithm formulated by
merging the concepts of both SMA [32] and WHO [33], respectively.
3.3. KNN
The KNN [39] is a simple machine learning model that is good in regression as well as
classification. The KNN is a supervised learning model, wherein N data points are split K -
count of clusters.
The following steps will explain how the K-Means algorithm works:
Step 1: Initially, the K count of neighbours are selected. In this research work, the K value is
set as 3 (normal, benign and malignant).
Step 2:for K count of neighbours the Euclidean distance is computed.
2
1
2
2
1
2 )
(
)
( Y
Y
X
X
Dist −
+
−
= (2)
Step 3: as per the computed Euclidean distance, K -nearest neighbor is chosen.
Step 4: among these K neighbors, the data point’s count in every category is computed
Step 5: Reverse the third steps, reassigning every datapoint to the cluster's new nearest centroid.
Step-6: If there is indeed a reassignment, go over to step-4; otherwise, go to FINISH.
Step 7: Terminate
The classified image acquired from KNN is denoted as KNN
out .

3.4. SVM
The SVM [40] is a supervised learning model that is being utilized for performing both the
regression as well as classification purpose. The ‘decision planes’ concept is the principle
behind the working of SVM. In ‘decision planes’, the hyperplanes are being utilized for
classifying the data features. The hyperplane is generated in an iterative manner with the
intention of lessening the classification errors. The SVM is good in classifying data in high-
dimensional space.
3.5. RF
The decision tree is the core unit behind the random forest classifier [41]. In fact, the decision
trees are the hierarchical structures that are being constructed with the data features. On the
basis of the entropy, the splitting of the nodes was accomplished for the selected sub-set of the
features. In the random forest there is a voting strategy, which is significant in overcoming the
undesirable properties and thereby aids in lessening the overfitting problem that takes place
during the training process. During the training process, the mechanism of bagging is being
introduced to the individual tress. The outcome from the RF exhibited the classified outcomes.
3.6. CNN-GRU
CNN [42] [43] is a newly developed classifier with three layers: fully connected, pooling, GRU
and convolution (multiple hidden layers). To calculate distinct feature maps, the convolution
layer encompasses numerous convolution kernels. The entire feature map is obtained by
combining multiple distinct kernels. The feature values at position ( )
y
x, referred to as q
z
y
x
M ,
, are
computed in the th
q layer corresponding to the th
n feature map.
Here, L points to the loss function, which needs to be as low as possible. This reduction in
the loss function of CNN-GRU (the final decision maker) is the majopr objective fo this
research work. Therefore, the classification accuracy of the projected model will be higher.
Mathematically, the objective function is denoted as per Eq. (4). Moreover, in Eq. (3), the
parameters( )
 relates to the desired R -count of input-output relations ( ) ( )
( )  
 
R
n
yi
out n
n
,
,
1
;
, 

Here, th
n input data to CNN ( )
n
out , the corresponding target labels ( )
n
O and the samples ( )
n
Obs . .
( ) ( )
( )

=
=
R
n
n
n
Obs
O
q
R
L
1
,
;
1

(3)
)
min(L
Obj = (4)
The weight of CNN is fine-tuned using the newly projected hybrid optimization model. The
projected hybrid model is the conceptual blend of the standard SMA and WHO, respectively.
Based on the oscillation mode of slime mould in nature, the SMA has been presented. The
WHO algorithm is based on how migratory Wildebeest herds effectively seek wide expanses
of grasslands for spots with high food density. In literature, it has been suggested that the
hybridization of the standard optimization models will enhance the convergence speed of the
solutions. Therefore, the most effective optimization models, the SMA as well as WHO has
been hybridized in this research work, The newly formulated model is referred as SUWO. The
weight of CNN-GRU enters as input to SUWO model.
4. RESULTS AND DISCUSSION
The proposed automatic BC detection model has been implemented in PYTHON. the projected
model has been validated with two datasets: MIAS-dataset [33] and DDSM-dataset [34].
“Images and labels / annotations for mammography scans make up the data [33]. MIAS has

further information regarding the database. The 'Preview' kernel demonstrates how to
appropriately parse the Info.txt and PGM files." Negative photos from the DDSM dataset and
positive images from the CBIS-DDSM dataset make up the dataset [34]. The data was pre-
processed to create pictures at a resolution of 299x299 pixels. The negative (DDSM) photos
were tiled into 598x598 tiles before being reduced to 299x299 pixels”. The sample images, pre-
processed images and segmented images are manifested in Fig.2. Moreover, 70% of the data
was utilized to train the proposed work, while the remaining 30% was used to test the proposed
work. The evaluations have been undergone by varying the learning rate from 60, 70, 80 and
90, respectively. The proposed classifier as well as algorithm is evaluated in terms of positive
performance (“specificity, sensitivity, precision and accuracy”) as well as “negative
performance (FPR, FNR) and other measures (F1-score and MCC)”. The positive measures of
the like specificity, sensitivity, precision and accuracy needs to be maintained as high as
possible, while the negative measures like FPR, FNR needs to be less. The projected model is
compared over the existing models like SMA+EC, WHO+EC, PRO+EC and TOA+EC,
respectively
original K-means Clustering FCM-Clustering CD-FCM
DDSM
(a) (b) (a) (b)
MIAS
(a) (b) (a) (b)
Figure 2 Sample Images, Pre-processed and Segmneted images
The convergence analysis is undergone to validate the efficiency of the projected SUWO
model over the existing models. Here, the convergence analysis is undergone for both the
dataset1and dataset2. The results acquired in terms of convergence analysis are manifested in
Fig.5. The evaluation is carried out between the SUWO over the existing models like TOA,
PRO, WHO and SMA, respectively. All these evaluation shave been carried out by varying the
count of iterations. On analysing the acquired outcomes, the projected model has recorded the
minimal cost function for every variation in the iteration count. In case of dataset1, the projected
model has recorded the utmost minimal value as 1.038, which is the least value when compared
to TOA=1.05 PRO=1.053, WHO=1.048 and SMA=1.043 at 100th iteration. On the other hand,
the projected model has recorded the most minimal cost function 8under every variation in
iteration count for dataset2. At the 100th iteration, the projected model has recorded the least
cost function as 1.02, which is the better value when compared to TOA=1.07, PRO=1.053,
WHO=1.065 and SMA=1.04. therefore, the projected Slimemould Updated Wildbeast
Optimization (SUWO) model is said to be highly convergent over the existing model, and
therefore the projected model is said to eb highly applicable for BC classification. This
enhancement is due to the improvement made within the SME model with WHO model.

(a) (b)
Figure 3 Convergence Analysis of proposed work for (a) Dataset1 and (b) dataset 2
This evaluation has been made at the 70th
training percentage as shown in the Table 1. In
case of dataset 1, the accuracy of the projected model is 21.2%, 7.5%, 7.5% and 7.5% improved
over the existing models like CNN, NN[4], DBN, MSVM[44], respectively. Moreover, the
sensitivity of the projected model is 9.09%, 15.15%, 15.15% and 15.15
Table 1 Classifier Performance Analysis for MIAS-Dataset
SUWO+EC CNN NN[4] DBN MSVM[44]
Accuracy (%) 0.924242 0.863636 0.757576 0.878788 0.787879
Sensitivity (%) 0.848485 0.903846 0.909091 0.826087 0.909091
Specificity(%) 1 0.714286 0.454545 1 0.545455
Precision(%) 1 0.921569 0.769231 1 0.8
F-Measure(%) 0.918033 0.912621 0.833333 0.904762 0.851064
MCC(%) 0.858395 0.602998 0.419314 0.768155 0.5
NPV(%) 0.868421 0.666667 0.714286 0.714286 0.75
FPR(%) 0 0.285714 0.545455 0 0.454545
FNR(%) 0.151515 0.096154 0.090909 0.173913 0.090909
5. CONCLUSION
This paper has projected an intelligent breast cancer diagnosis approach by following four
major phases viz., “(a)Pre-processing, (b) Segmentation, (c) Feature Extraction and (d)
classification”. The collected raw mammogram images were pre-processed via median filtering
and CLAHE. The pre-processed image were subjected to segmentation via CD-FCM.
Subsequently the features like ALVP, Shape features and GLCM were extracted. From the
extracted features, the most optimal ones is selected using the Improved information gain.
Finally, the classification process is carried out via an ensemble classification technique. This
ensemble classification technique encapsulates the optimized CNN-GRU, SVM, RF and KNN,
respectively. The SVM, RF and KNN are trained with the selected relevant features. The CNN-
GRU is the final decision maker, and it is trained with the outcomes acquired from SVM, RF
and KNN. In order to enhance the detection accuracy of CNN-GRU, the weight function of
CNN-GRU is optimized using a newly developed SUWO that has been formulated by merging
the concepts of both SMA and WHO, respectively. Finally, a comparative evaluation has been
undergone to validate the efficiency of the projected model. The accuracy of the projected
model under dataset 1 is 97%, which is 12.37%, 17.52%, 22.6% and 19.5% improved over the
existing models like SMA+EC, WHO+EC, PRO+EC and TOA+EC, respectively at 90th
learning percentage for dataset1.

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