Breast Cancer Automatic Diagnosis System using Faster Regional

Convolutional Neural Networks

Lourdes Duran-Lopez, Juan Pedro Dominguez-Morales

, Isabel Amaya-Rodriguez,

Francisco Luna-Perejon, Javier Civit-Masot, Saturnino Vicente-Diaz

and Alejandro Linares-Barranco

Robotics and Technology of Computers Lab., University of Seville, Seville 41012, Spain

Keywords:

Breast Cancer, Mammography, Deep Learning, Convolutional Neural Network, Faster Regional

Convolutional Neural Network, Medical Image Analysis.

Abstract:

Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer,

the mammography is used as the most efﬁcient technique to identify abnormalities such as tumors. Automatic

detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors

in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional

Convolutional Neural Networks are able to determine the presence of an object and also its position inside

the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in

mammogram images and propose a detection system that contains: (1) a preprocessing step performed on

mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural

Network model, which performs feature extraction over the mammograms in order to locate tumors within

each image and classify them as malignant or benign. The results obtained show that the proposed algorithm

has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians

when detecting tumors from mammogram images.

1 INTRODUCTION

Breast cancer is the most prevalent cancer among

women and the leading cause of cancer death. Ac-

cording to Global Cancer Observatory (GLOBO-

CAN), there have been around 2.1 million diagnosed

female breast cancer cases in 2018, representing al-

most one for every four cancer cases among women

(Bray et al., 2018). There are several types of abnor-

malities in breast cancer such as tumors and micro-

calciﬁcations, which are the main indicators of malig-

nancy. Tumors are attributed to any lesion or protu-

berance in the breast, which may be benign or malig-

nant; while microcalciﬁcations are areas with a large

amount of calcium accumulation. The early detection

of these abnormalities is crucial to improve women’s

quality of life and also the survival rate in critical

cases.

Mammography is the most effective screening

https://orcid.org/0000-0002-5474-107X

https://orcid.org/0000-0001-9466-485X

https://orcid.org/0000-0002-6056-740X

tool for the diagnosis of breast cancer. When us-

ing this technique, the traditional method to perform

the diagnose consists in the evaluation by a physi-

cian. However, due to certain reasons, the diagnosis

of breast cancer may be susceptible to failure. On the

one hand, the experience of the physician and his ex-

pertise, and also the fatigue occasioned after examin-

ing many consecutive mammograms are some of the

reasons for failure. On the other hand, there may be

other reasons beyond the ones related to the special-

ist. Tumors could be confusing and hard to classify

as benign or malignant depending on the character-

istics shown in the mammograms due to their high

similarity. To this end, a computer-aided diagnosis

(CAD) could provide a second opinion in order to

assist physicians to make the decision, which could

reduce false negative diagnoses and, therefore, mini-

mize the possibility of making a mistake.

In this work, a CAD system for detecting and clas-

sifying tumors in mammograms using a type of Deep

Learning-based algorithm called Regional Convolu-

tional Neural Networks (R-CNN) is presented. The

purpose of R-CNNs is to locate an object inside an

444

Duran-Lopez, L., Dominguez-Morales, J., Amaya-Rodriguez, I., Luna-Perejon, F., Civit-Masot, J., Vicente-Diaz, S. and Linares-Barranco, A.

Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks.

DOI: 10.5220/0008494304440448

In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019), pages 444-448

ISBN: 978-989-758-384-1

image by proposing possible regions of interest and

classifying them later. Faster Regional Convolutional

Neural Network (Faster R-CNN) (Ren et al., 2015)

is one of the most recent developed algorithms based

on the R-CNN architecture. Faster R-CNN was devel-

oped in 2015 and its objective was to reduce its prede-

cessors computation time. This algorithm has already

been used for different context, proving its ability to

detect objects successfully. Face detection (Jiang and

Learned-Miller, 2017), polyp detection in gastroin-

testinal images, driver’s cell-phone usage, hands on

steering wheel detection (Hoang Ngan Le et al., 2016)

and wildland forest ﬁre smoke detection (Zhang et al.,

2018) are some application examples.

In this paper, we report our study of an automated

computerized system for locating and classifying tu-

mors as malignant or benign in mammograms using a

Faster R-CNN algorithm.

The rest of the paper is structured as follows: Sec-

tion 2 is organized in two main subsections: Dataset

(2.1), which consists of Image Acquisition (2.1.1)

and Data Preprocessing (2.1.2), and Neural Network

(2.2). Then, the results achieved in this approach are

presented and discussed in Section 3. Finally, the con-

clusions of this work are presented in Section 4.

2 METHODOLOGY

In this section, we present the methods that are used in

the approach that has been carried out. First, the used

dataset is explained from its acquisition to its pre-

processing. Finally, the Faster R-CNN is presented,

along with the architecture, training and testing of the

neural network that has been used in this work, and

also, the performance evaluation metrics.

2.1 Dataset

2.1.1 Image Acquisition

In this work, the database of mammograms from Dig-

ital Database for Screening Mammography (DDSM)

was used. The DDSM (Heath et al., 2000), (Heath

et al., 1998) was created by the University of South

Florida. It has become a very useful tool in the devel-

opment of decision support systems for breast cancer

diagnosis. DDSM contains more than 2620 scanned

grayscale mammogram images which include nor-

mal, benign and malignant cases with veriﬁed patho-

logical information (see Fig. 1). For each case, four

http://www.eng.usf.edu/cvprg/Mammography/

Database.html

Figure 1: Mammograms taken from the DDSM. From left

to right: breast without evidence of abnormality, breast with

presence of malignant tumor and breast with benign tumor

both of them shown in red.

mammograms are taken with two different views: bi-

lateral craniocaudal (CC) and mediolateral oblique

(MLO). In benign and malignant cases, ground truth

information is given with the location of the abnor-

mality.

2.1.2 Data Preprocessing

A preprocessing step was applied to the images in or-

der to enhance the performance of the system and,

therefore, improve the results of the detection (see

Fig. 2).

Grayscale images tend to have a compressed his-

togram distribution, meaning that the details are not

easy to observe. By modifying the histogram, the

grayscale interval can be extended, increasing the

contrast in order to better distinguish details con-

tained in the image. Therefore, a contrast enhance-

ment method called contrast-limited adaptative his-

togram equalization (CLAHE) was used to enhance

image details. CLAHE is able to deﬁne the shape

of the histogram that produces the best quality result

(Maitra et al., 2012).

Another problem that has to be considered is the

noise, since during the mammogram acquisition it is

one of the effects that is going to be present. There-

fore, we performed the spatial transformation using

the median ﬁlter in order to reduce noise (Ponraj et al.,

2011). This technique generates a new image where

each pixel gets its intensity from the median of neigh-

boring pixels.

Mammograms of the DDSM have different size,

thus the preprocessed dataset was resized to 600 width

and 900 pixels height. Also, a normalization process

was applied reducing the range of values to [0,1] in

order to achieve a better performance.

Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

445

Figure 2: Preprocessing step applied to the original mam-

mograms. (A) shows an original mammogram sample,

while (B) shows the same mammogram after being prepro-

cessed.

2.2 Neural Network Model for Breast

Cancer Detection

As mentioned before, Faster R-CNN is an algorithm

whose purpose is to detect and classify the regions of

interest, locating them in the image. This algorithm

consists of two parts (see Fig. 3): the region proposal

network (RPN) which generates proposal regions of

interest and the detector network whose purpose is

to perform the classiﬁcation over the proposed region

(Ren et al., 2015).

RPN receives an image from the dataset as an in-

put. Then, it extracts feature maps and analyzes them

to propose the regions that most likely contains a tu-

mor. The novel step that this architecture introduces is

the way to determine the regions of interest, by using

a Convolutional Neural Network that takes advantage

of the mathematical operations made in the convolu-

tion layers. In this study, ResNet50 (He et al., 2016)

was used as the CNN model.

The proposed regions of interest generated by the

RPN are the input of the detector network, called

Faster R-CNN detector, which performs two main

tasks: a classiﬁcation and a regression. The output of

the regression determines a predicted bounding-box

where the object could be located, while the output of

the classiﬁcation sub-network is the probability (con-

ﬁdence value) that the box contains the object.

For the training step, all the images obtained af-

ter performing the preprocessing step were randomly

mixed in order to avoid any classiﬁcation bias.

To evaluate the accuracy and robustness of Faster

R-CNN in the detection, 85% of the preprocessed

dataset was used to train and validate the network,

while the remaining 15% was used to test its perfor-

mance. These two folders did not contain mammo-

grams from the same patient, meaning that the per-

formance of the system was not tested using images

from patients that were previously used in the training

step.

In this study, TensorFlow

together with Keras

have been used to design, train and test the network.

3 RESULTS AND DISCUSSION

In this section, the results obtained after testing the

trained network with the images from the dataset are

presented.

In order to evaluate the results, the accuracy was

deﬁned using the following equation:

Accuracy = 100 ×

T P + T N

T P + T N + FP + FN

(1)

Where TP means true positives, TN means true

negatives, FP means false positives, and FN means

false negatives.

Every time that the training step used the whole

amount of images that were selected for training the

network as input, the system performed a test over

the a validation set. After training the network until

the loss value was minimum, the classiﬁer achieved a

mean accuracy of 97.375% over the two classes that

has been studied in this work: benign and malignant

tumor.

After obtaining these results, the output images

from the network were analyzed in order to see the

bounding boxes that the system proposed over the

original images. Fig. 4 shows the results of our recog-

nition system in terms of precision when detecting tu-

mors inside the samples from the dataset that were

considered for testing it. Images in the left part of Fig.

4 correspond to the output of our system, where the

bounding boxes are marked in blue; and, in the right

part, their corresponding mark images are presented,

indicating where tumors are located, considering that

as ground truth.

Most of the tumors inside the set of images were

not located exactly in the same area as the ground

truth, which could be caused by some factors. First

of all, the amount of images that were considered for

training the network was very low, which could lead

to the fact that the network has not learned how to de-

tect tumors properly. Also, training the network takes

several days, which didn’t let us experiment enough

https://www.tensorﬂow.org/?hl=es

https://keras.io/

NCTA 2019 - 11th International Conference on Neural Computation Theory and Applications

446

Figure 3: Faster R-CNN architecture.

Figure 4: Results of the network performance. At the

left, mammograms with the predicted tumors and its cor-

responding conﬁdence values (benign tumor shown in blue,

malignant tumors shown in green). At the right, ground

truth images indicating where the tumors are located. A

and B were correctly predicted whereas C was not.

to optimize the hyperparameters for this task. Finally,

only the ResNet50 model was used, leaving the door

open for many other different architectures.

Other approaches as Ayelet Akselrod-Ballin et al.

(Akselrod-Ballin et al., 2016) developed a modiﬁed

algorithm based on Faster R-CNN whose purpose is

to detect and classify the major clinical classes in

breast cancer, malignant and benign tumors. As op-

posed to our implementation, that architecture uses

VGG16 as CNN for the extraction of features and pro-

posal of regions of interest. This system uses multi-

center clinical dataset with a total of 4750 images.

After training their modiﬁed network, they obtained

a 77% accuracy.

The preliminary results that are presented in this

work already prove that, if we take into consideration

the drawbacks that were mentioned before, the system

is already able to classify if the mammogram has a

benign or a malignant tumor with an accuracy that is

higher than 97%.

4 CONCLUSIONS

In this study, a computerized-aided diagnosis method

based on Faster R-CNN used for detecting and clas-

sifying tumors in mammograms is presented. Firstly,

the images to train the network were obtained from

the public database DDSM, which were preprocessed

in order to improve the results. This preprocessing

step consisted of an enhancement of the images by

increasing the contrast with the CLAHE technique,

a noise reduction with the median ﬁlter and a resize

and normalization processes. Then, the network was

trained and validated with the 85% of the prepro-

cessed dataset, which extracts features over the mam-

mogram images, proposes regions of interest where

tumors could be located, and, ﬁnally classiﬁes each

Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

447

of them based on the probability that they contain a

tumor. Using the detection metrics, the performance

of the network was measured with the remaining 15%

of the dataset in order to evaluate its robustness. After

training the network, the results show that this pro-

posed computer-aided diagnosis method achieved a

mean accuracy of 97.375% proving that the system

could aid specialized doctors to recognize cancerous

signs when analyzing mammograms, improving pa-

tients’ quality of life.

In future works, the authors will study different

CNNs models and also other network architectures

like Mask R-CNNs instead of Faster R-CNNs in order

to not only locate the tumor inside the mammogram,

but also to create a mask with its shape. This way, the

size of the tumor can be estimated more precisely and

taken into account in the decision making task.

ACKNOWLEDGEMENTS

This work was supported by the excellence project

from the Spanish government grant (with support

from the European Regional Development Fund)

COFNET (TEC2016-77785-P).

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