Classification of Breast Masses Using Convolutional Neural Network as Feature Extractor and Classifier

Classiﬁcation of Breast Masses Using
Convolutional Neural Network as
Feature Extractor and Classiﬁer
Pinaki Ranjan Sarkara
, Deepak Mishraa
and Gorthi R.K.S.S Manyamb
a,b: Indian Institute of Space Science and Technology, Trivandrum
Indian Institute of Technology, Tirupati
Second International Conference on Computer Vision and
Image Processing & Workshop on Multimedia, IIT-Roorkee
10 September, 2017

Outline
1 Introduction
Motivation
Objective
Difficulties
2 Background Theories
Deep Learning
3 Approach
Databases & Pre-processing
Extraction of Region of Interests
Overall Architecture for Mass Classification
Feature Extraction & Classification
4 Results
Results on different databases
Contribution
5 Scope of Future Works
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 2/24

Introduction Motivation
Motivation
Motivation
Breast is the most common site of cancer among women in
India (27% of total). Studies show that nearly 48.45%
patients died in the year 2012ab.
Due to very small non-palpable micro-calcification
clusters/masses, approximately 1% to 20% of breast cancer is
missed by radiologists.
Due to the difficulties of Radiologists to detect
micro-calcification clusters a Computer Aided Diagnosis
(CAD) system is much needed.
a
Trends of breast cancer in India, http://www.breastcancerindia.net/statistics/trends.html.
b
Siegel R.L. Miller; K.D. Jemal; A, “Cancer statistics” 2017,
CA: A Cancer Journal for Clinicians 67(1) 730 (2017).

Introduction Objective
Objective
Objectives
Automatic mass classiﬁcation from breast mammograms.
Low False Positive Rate and High True Positive Rate in
classiﬁcation.
Figure 1: Objective in this work

Introduction Difficulties
Difficulties
Figure 2: The levels 1, 2 and 3 of ROIs represents normal, benign and malignant
classes respectively1. From these figures, it is understood that classification between
them is very difficult.
1
S. Beura, B. Majhi, and R. Dash,
“Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for dete
” Neurocomputing, vol. 154, pp. 114, 2015.

Background Theories Deep Learning
Deep Learning
“Deep Learning is an algorithm which has no theoretical limitations of
what it can learn; the more data you give and the more computational
time you provide, the better it is.”
- Geoﬀrey Hinton, Google
Deep learning maybe loosely deﬁned as an attempt to train a
hierarchy of feature detectors with each layer learning a higher
representation of the preceding layer.
Deep learning discovers intricate structure in large data sets by
using the backpropagation algorithm to indicate how a machine
should change its internal parameters that are used to compute the
representation in each layer from the representation in the previous
layer2
.
2
LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. Nature, 521(7553), pp.436-444..

Successful Architectures in DL
Many variants of deep learning architectures are being proposed
and some of them are proved to be successful such as:
Convolutional Neural Network (CNN)3
Deep Boltzmann Machine (DBM)4
Deep Belief Networks (DBN)5
Stacked Denoising Auto-encoders (SDAE)6
3
A. Krizhevsky, “Imagenet classiﬁcation with deep convolutional neural networks”.
4
R. Salakhutdinov and G. E. Hinton, “Deep boltzmann machines, ” in AISTATS, vol. 1, p. 3, 2009.
5
G. E. Hinton, “Deep belief networks, ” Scholarpedia, vol. 4, no. 5, p. 5947, 2009.
6
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol,
“Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion”.

Convolutional Neural Network
Figure 3: The CNN architecture is composed hierarchical units and each unit extracts
different level of features. Combining more units will produce deeper network along
with more semantic features.7
7
P.R.Sarkar, Deepak Mishra. and Gorthi R.K.S.S. Manyam,
“Classification of Breast Masses Using Convolutional Neural Network as Feature Extractor and Classifier”. CVIP-2017.

Approach Databases & Pre-processing
Databases & Pre-processing
We validated the proposed method using two publicly available
databases i.e. Mammographic Image Analysis Society (MIAS)8
and
CBIS-DDSM9
.
Within 2620 scanned cases based on the magnitude of abnormality,
the abnormal class is divided into two more classes, benign and
malignant. We have taken total 273 benign cases and 273
malignant cases from CBIS-DDSM.
From 322 cases of mini-MIAS database 64 benign and 51 malignant
cases are there in the abnormal class.
Each ROIs are resized to 84 × 84 to reduce the number of network
parameters also we used four rotations rot = {0o
, 90o
, 180o
, 270o
}
and increased the training data three times.
8
J. Suckling, J. Parker, D. Dance, S. Astley, I. Hutt, C. Boggis, I. Ricketts, E. Stamatakis, N. Cerneaz,
S. Kok, and others, “The mammographic image analysis society digital mammogram database,
” in Exerpta Medica. International Congress Series, vol. 1069, pp. 375378, 1994..
9
R. S. Lee and D. R. Francisco Gimenez, Assaf Hoogi, “Curated Breast Imaging Subset of DDSM,
” in The Cancer Imaging Archive, 2016..

Approach Extraction of Region of Interests
Extraction of Region of Interests
In this work, we evaluated our model on pre-segmented ROIs.
CBIS-DDSM database oﬀers segmented ROIs for the
development of CAD systems.
A cropping operation has been applied on MIAS mammogram
images to extract the regions of interests (ROIs) which
contain the abnormalities, excluding the unwanted portions of
the image.

Approach Overall Architecture for Mass Classification
Overall Architecture for Mass Classification
Figure 4: Overall architecture of a complete breast mass classification framework. The
detection and localization network detects the mass in the mammogram then a
bounding-box regression is applied to get the mass ROI. The ROI is given to our
network to extract deep features for the classification task.

Detailed Parameters of Each Layers
Table 1: Detailed parameters of each layer
Name Filter size Depth of Filter Dropout
Conv1 11 32
ReLU1 1
Pooling1 3
Conv2 11 64
ReLU2 1
Dropout1 0.5
Pooling2 3
Conv3 7 96
ReLU3 1
Dropout2 0.15
Pooling3 2
Conv4 5 128
ReLU4 1
Dropout3 0.25
Pooling4 2
Fc5 1 5000
ReLU5 1
Dropout4 0.5
Fc6 1 1000
ReLU6 1
Fc7 1 2
Softmax 1

Training process
Here we try to minimize an objective function (in this work,
categorical cross entropy) through gradient descent algorithm.
Studies show that stochastic gradient descent (SGD) is very
good at optimizing the objective functions for DL. We have
used SGD for this work.
Parameters are updated using backpropagation algorithm.
After getting satisfactory traing accuracy and loss value, we
ensure that our network is well trained.
The trained network is used for testing new inputs and can be
ﬁne-tuned on another databases like Inbreast, BCDR etc.

Approach Feature Extraction & Classification
Feature Extraction & Classification
CNN as classifier
Softmax is used for producing probability scores for each class.
Softmax works on the whole data cloud where SVM works only on
the data near to support vector.
SVM as classifier
We have used our trained CNN network as a deep feature extractor
as well as a classifier.
The deep feature is a 1000 × 1 dimensional feature vector and used
SVM with Radial Basis Function (RBF) kernel.
A grid search method is employed to find the best hyperparameters
w.r.t RBF kernel within a range.

Results Results on different databases
Result in MIAS Database
(a) Training and validation accuracy in
mini-MIAS database
(b) Training and validation loss in
mini-MIAS database
Figure 5: Training results in mini-MIAS database

Result in DDSM Database
(a) Training and validation accuracy in
CBIS-DDSM database
(b) Training and validation loss in
CBIS-DDSM database
Figure 6: Training results in CBIS-DDSM database

ROC curve analysis
0.0 0.2 0.4 0.6 0.8 1.0
False Positive Rate
0.0
0.2
0.4
0.6
0.8
1.0
TruePositiveRate
SVM as classifier, AUC = 0.9768
CNN as classifier, AUC = 0.9921
(a) ROC curve of classiﬁcation in
mini-MIAS database
0.0 0.2 0.4 0.6 0.8 1.0
False Positive Rate
0.0
0.2
0.4
0.6
0.8
1.0
TruePositiveRate
SVM as classifier, AUC = 0.9922
CNN as classifier, AUC = 0.9993
(b) ROC curve of classiﬁcation in
CBIS-DDSM database
Figure 7: Receiver Operating Characteristics curve analysis

Evaluation metrics in different databases
k-fold Accuracy Sensitivity Specificity
(MIAS) (%) (%) (%)
2 97.701 95.556 100
3 97.637 98.4375 99.82
4 99.422 98.901 100
5 99.074 98.261 100
(DDSM) (%) (%) (%)
2 98.627 97.757 99.532
3 98.171 96.375 100
4 98.970 99.078 98.863
5 99.267 98.564 100
Table 2: Classification results using k-fold cross-validation (CNN as classifier)
(MIAS) (%) (%) (%)
2 100 100 100
3 100 100 100
4 99.422 98.901 100
5 99.074 98.261 100
(DDSM) (%) (%) (%)
2 99.314 98.643 100
3 99.238 98.507 100
4 98.856 97.738 100
5 98.260 96.975 99.975
Table 3: Classification results using k-fold cross-validation (SVM as classifier)

Merits of using CNN for CADs
Why this method?
CNNs are very good at learning intricate strictures from the
high-dimensional data.
It extracts semantic features which simulates the real
diagnosis process of the mammograms.
Our main idea was to show that, the CNN works as the
feature extractor as well as the classiﬁer for this problem.

Contribution
References Techniques Database Classification
performance
Xie et. al.10 Gray level features, MIAS 96.0%
textural features DDSM 95.7%
Jiao et. al.11 High & medium level, DDSM 96.7%
deep features
Arevalo et. al.12 CNN, SVM DDSM 96.7%
Beura et. al.13 2D-DWT, GLCM MIAS 98.0%
DDSM 98.8%
Ours Segmented ROIs, MIAS 99.081%
CNN DDSM 99.267%
Table 4: Comparison of classification performances
10
W. Xie, Y. Li, and Y. Ma,
“Breast mass classification in digital mammography based on extreme learning machine, ” Neurocomputing,
vol. 173, pp. 930941, 2016..
11
Z. Jiao, X. Gao, Y. Wang, and J. Li, “A deep feature based framework for breast masses classification,
” Neurocomputing, vol. 197, pp. 221231, July 2016..
12
J. Arevalo, F. A. Gonz alez, R. Ramos-Poll an, J. L. Oliveira, and M. A. G. Lopez,
Convolutional neural networks for mammography mass lesion classification,
in Engineering in Medicine and Biology Society (EMBC),
2015 37th Annual International Conference of the IEEE, pp. 797800, IEEE, 2015..
13
S. Beura, B. Majhi, and R. Dash,
“Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for dete
” Neurocomputing, vol. 154, pp. 114, 2015..

Scope of Future Works
Some more contributions in this work
Towards end-to-end CAD system
Though in this work, we have used pre-segmented ROIs for
training ans testing of mammograms, later we used a fully
convolutional deep hierarchical saliency map prediction
network.
Here, we treat the masses as salient objects and predicts the
suspicious regions through this trained network.
This leads us to propose an end-to-end breast mass
classiﬁcation framework.

End-to-end Breast Mass Classiﬁcation Framework
The main objective of an end-to-end computer aided diagnosis
system is to improve the classiﬁcation accuracy while minimizing
the false positives. One can improve our novel architecture for
better simulating the diagnosis procedure followed by radiologists

Questions?
sarkar0499pinaki@gmail.com

Classification of Breast Masses Using Convolutional Neural Network as Feature Extractor and Classifier

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