Breast Cancer Histopathological Image Classification using

Progressive Resizing Approach

Hendra Bunyamin

, Hapnes Toba

, Meyliana

and Roro Wahyudianingsih

Informatics Engineering, Maranatha Christian University, Jl Prof. drg. Surya Sumantri, M.P.H. No. 65, Bandung, Indonesia

Accounting Department, Maranatha Christian University, Jl Prof. drg. Surya Sumantri, M.P.H. No. 65, Bandung, Indonesia

Faculty of Medicine, Maranatha Christian University, Jl Prof. drg. Surya Sumantri, M.P.H. No. 65, Bandung, Indonesia

Keywords: Breast Cancer Histopathological Image Classification, Deep Learning, Convolutional Neural Network, Pro-

gressive Resizing, Vahadane Image Normalization.

Abstract: Breast cancer (BC) is a lethal disease which causes the second largest number of deaths among women in the

world. A diagnosis of biopsy tissue stained with hematoxylin & eosin, commonly named BC histopathological

image, is a non-trivial task which requires a specialist to interpret. Recently, the advance in machine learning

techniques driven by deep learning techniques and competition datasets has enabled the automation and pre-

diction of histopathological images interpretation. Each different competition dataset has its own state-of-the-

art technique; therefore, this paper explores an avenue of research by merging popular BC histopathological

images research datasets and searching for the best performing models on the unified dataset. The merging

process maintains similar classes among the datasets; consequently, the unified dataset has three classes and

the prediction problem is cast into multi-class classification problem. We propose a combination of Vahadane

preprocessing technique and training method using progressive resizing approach. Our approach demonstrates

that both utilizing Vahadane image normalization and utilizing our progressive resizing technique achieve

around 99% in F

score , which is comparable among other state-of-the-art models. The unified dataset is also

provided online for advancing research in histopathological images interpretation.

INTRODUCTION

Breast cancer (BC) is the cause of the second largest

deaths among women (Spanhol et al., 2016; Bray et

al., 2018; McKinney et al., 2020) in the world. Data

Global Cancer Observatory 2018 from World Health

Organization (WHO) showed that the most number of

cancer cases in Indonesia is BC with 58,256 of

348,809 patients or 16.7% (World Health

Organization, 2018). However, cancer screening re-

search encompassing imaging procedures, such as di-

agnostic mammogram (X-rays), magnetic resonance

imaging, ultrasound (sonography), and thermography

has been conducted for more than four decades and

deemed beneficial to likely lessen number of deaths

caused by BC significantly (Stenkvist et al., 1978;

Institute of Medicine and National Research Council,

https://orcid.org/0000-0002-5150-4394

https://orcid.org/0000-0003-0586-8880

https://orcid.org/0000-0001-6198-7484

https://orcid.org/0000-0002-0800-9937

2005; Tabar et al.

2011

;

Canadian Task Force on

Preventive Health Care, 2011; Marmot et al., 2013).

Furthermore, patients with suspected breast

cancer need to have a biopsy which is frequently used

to con- firm the diagnosis before treatment is planned

(Millis, 1984).

Generally, a biopsy is conducted by taking a

sample of tissue from a suspicious area. Next, the

sample is stained with hematoxylin and eosin (H&E)

substance used to differentiate a nucleus with a

parenchyma. The difference can be observed through

an optical microscope; moreover, the images seen

through the microscope are captured as giga-pixel

images and commonly named as whole-slide images

(WSI) for further digital image processing, such as

cancer cell segmentation or detection (Aresta et al.,

2019).

Bunyamin, H., Toba, H., Meyliana, . and Wahyudianingsih, R.

Breast Cancer Histopathological Image Classiﬁcation using Progressive Resizing Approach.

DOI: 10.5220/0010754100003113

In Proceedings of the 1st International Conference on Emerging Issues in Technology, Engineering and Science (ICE-TES 2021), pages 351-357

ISBN: 978-989-758-601-9

351

Biopsy diagnosis from histopathological images

is considered a gold standard for determining al most

all types of cancer, specifically BC International

Agency for Research on Cancer, 2012; David

Figure 1: An image of malignant cells.

Figure 2: An image of benign cells.

Figure 3: An image of normal cells.

S. Strayer, 2014). Properties of the histo-

pathological images are analyzed by pathologists in

order to find types of the breast cancers. Figure 1 and

Figure 2 show malignant and benign cancer cells

while Figure 3 shows normal cells.

In developing countries, such as Indonesia, the

ratio of physician to population is still lower than the

WHO-recommended figure; moreover, ongoing geo-

graphical disparities exist which exacerbate the short-

age of physicians (Asia Pacific Observatory on

Health Systems and Policies, 2017). With the advent

of Artificial Intelligence (AI), particularly deep

learning, there are vast opportunities to help classify

types of BC with some degrees of automation.

are datasets which charge the deep learning algo-

rithms. Fortunately, international research organiza-

tions such as Pathological Anatomy and Cytopathol-

ogy (P&D) Laboratorium in Parana, Brazil Spanhol

et al., 2016), the Institute of Molecular Pathology and

Immunology of the University of Porto (IPA-

TIMUP) and the Institute for Research and

Innovation in Health (i3S) in Porto, Portugal (Aresta

et al., 2019), and Center for Bio-Image Informatics,

University of California in Santa Barbara, USA

(Gelasca et al., 2008) have released publicly available

BC histopathological image datasets.

Each publicly available dataset has its own char-

acteristic; furthermore, combining these characteris-

tics into a unified dataset will make a rich learning

dataset for deep learning models. A deep learning

model which learns from the unified dataset will be

better in generalization on unseen data than the one

learns from only one dataset (Geron, 2019

Therefore

firstly this paper also provides a unified

dataset as a new publicly available dataset to help this

research field progress.

Secondly, the paper initializes a deep learning

model which functions as a baseline on the new uni-

fied dataset. After the Vahadane preprocessing tech-

nique (Vahadane et al., 2016) is applied into our uni-

fied dataset, we feed the preprocessed dataset into a

model which employs a progressive resizing ap-

proach (Howard and Gugger, 2020). Both the

preprocessing technique and the resizing approach

are suitable to our problem; hence, the performance

of our model has soared and put the model among

state-of-the-art models for BC histopathological

image classification problem.

In the remainder of this paper, we first explore

related work which delves into various datasets and

relevant BC histopathological image classification

techniques. Next, we turn our attention to the

Vahadane preprocessing technique and progressive

resizing. Finally, we discuss our results and conclude

with a few general observations and directions for

future work

RELATED WORK

The related work of our paper discusses BC

histopathological images datasets and relevant BC

classification techniques.

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352

2.1

Histopathological Image Datasets

We have searched and found three publicly accessi-

ble datasets on the Internet that are BreaKHis (Span-

hol et al., 2016), BACH (Aresta et al., 2019), and

UCSB benchmark dataset for bioimaging applica-

tion (Gelasca et al., 2008).

The BreaKHis consists of 7,909 BC

histopathological

images taken from 82 patients from

January 2014 to December 2014 with different

magnifying factors (40, 100, 200, and 400) (Spanhol

et al., 2016). Moreover, the numbers of benign and

malignant cases are 2,480 and 5,429 respectively

The

benign category includes adenosis (A), fibroadenoma

(F), phyllodes tumor (PT), and tubular adenoma

(TA); additionally, the malignant category covers

ductal carcinoma (DC), lobular carcinoma (LC),

mucinous carcinoma (MC), and papillary carcinoma

(PC). Laboratorium P&D (Pathological Anatomy and

Cytopathology, Parana, Brazil) has published this

dataset for research purposes.

The BACH was released as a grand challenge on

BC histopathological images which is a part of the

15th International Conference on Image Analysis and

Recognition (ICIAR 2018). The annotation for the

dataset was done by pathologists from the Institute of

Molecular Pathology and Immunology of the

University of Porto (IPATIMUP) and the Institute for

Research and Innovation in Health (i3S) (Aresta et

al., 2019). The histopathological images were

obtained from patients in Porto and Castelo Branco

area, Portugal in year 2014, 2015, and 2017. The

dataset consists of microscopy dataset and whole-

slide images dataset; specifically, the whole-side

images dataset is used for an image segmentation task

besides. Our research employs the microscopy dataset

which categorizes BC cells into 1) benign, 2)

malignant, 3) in situ carcinoma, and 4) invasive

carcinoma. Furthermore, the dataset is composed of

400 training images and 100 test images with equal

number of images in each category.

The third dataset is University of California, Santa

Barbara (UCSB) benchmark dataset for bioimaging

application (Gelasca et al., 2008). The dataset

comprises of 58 histopathological images which are

used for BC histopathological image classification

task with associated ground truth data available.

2.2

Histopathological Classification

Techniques

Prior work has established several techniques to solve

BC histopathological images classification problem.

An early BC histopathological classification

technique begins with three extracted features from

images, that are curvelet transform, statistical data

from Gray Level Co-occurrence Matrix (GLCM), and

Completed Local Binary Patterns (CLBP) (Zhang et

al., 2011). Next, these three features construct an

input layer for Random Subspace Ensemble

(Skurichina and Duin, 2002).

The Random Subspace Ensemble in their research

comprises 20 multi-layer perceptron (MLP) whose

number of hidden layers are random values in range

between 30 and 50 inclusively. The accuracy of the

model has achieved 95.22% on the 3-class (normal, in

situ carcinoma, and invasive carcinoma) dataset

released by the Computer Science department of Israel

Institute of Technology which is, unfortunately, not

publicly available.

Other prior work employs a combination of Con-

volutional Neural Network (CNN) and a Support

Vector Machines classifier on BACH dataset (Araujo

et al., 2017). This combination model achieves

accuracies of 77.8% for four-class and 83.3% for

carcinoma/non-carcinoma BC classification tasks and

recall of 95.6%. Another work on BACH dataset

comes from combining several deep neural networks

and gradient boosted trees classifiers (Rakhlin et al.,

2018). The model gains 87.2% accuracy and 93.8%

accuracy for 4-class classification and 2-class

classification task respectively. Lastly, a work on

BACH dataset fine-tunes Inception-v3 CNN with a

strategy for image patches extraction (Golatkar et al.,

2018). The Inception-v3 model acquires accuracy of

85% over four classes and 93% for non-cancer

(normal or benign) vs. malignant (in situ or invasive

carcinoma). A current state-of-the-art technique,

which outperforms the previous work by Araujo et

al. (2017), Rakhlin et al. (2018), and Golatkar et al.

(2018) forms

a new hybrid convolutional and

recurrent deep neural network enriched with

multilevel feature representation of image patches

(Yan et al., 2020). This technique combines the

strengths of convolutional and recurrent neural

networks and preserves the short-term and long-

term spatial correlations among the patches. The

method achieves an average accuracy of 91.3% for 4-

class classification task. Unfortunately, the dataset of

this work is not available for outside of China.

RESEARCH METHODOLOGY

Our research methodology consists of merging the

dataset, preprocessing the dataset, building the

baseline, and constructing progressive resizing

approach.

Breast Cancer Histopathological Image Classiﬁcation using Progressive Resizing Approach

353

3.1

Merging the Dataset

Three different datasets (Spanhol et al., 2016; Aresta

et al., 2019; Gelasca et al., 2008) have different

classes; therefore, in order to merge those different

classes, those labels need adjusting. Firstly, the

classes

in BreaKHis dataset are benign and malig- nant. The

benign class has subclasses: adenosis, fi- broadenoma,

phyllodes tumor, and tubular adenoma, while the

malignant class consists of adenosis, fi- broadenoma,

phyllodes tumor, and tubular adenoma. Secondly,

BACH dataset have four classes, such as normal,

benign, in situ carcinoma, and invasive carci- noma.

Lastly, UCSB benchmark dataset has benign and

malignant classes. After considering the number of

classes, we opt to choose three classes, that are

normal, benign, and malignant. Specifically, both in

situ carcinoma and invasive carcinoma of the BACH

are joined into the malignant class of the new unified

dataset.

3.2

Color Normalization

Next, the Vahadane technique which combines

Sparse Non-negative Matrix Factorization (SNMF)

and Structure-preserving Color Normalization

(SPCN) (Vahadane et al., 2016) is applied into our

images. Particularly, the Vahadane technique solves

both stain separation and color normalization

problems. The stain separation problem is cast into a

non-negative matrix factorization (NMF) with an

addition of a sparseness constraint. Moreover, the

color basis of an image is substituted by the one

of a pathologist-preferred target image. This is the

algorithm of SPCN whose advantage is to maintain

the image’s original stain concentrations. The way

SPCN works is through replacing the color basis of a

source image with those of a pathologist-preferred

target image, while still maintaining its original stain

concentrations. The ﬂexibility to select a preferred

target appearance in different scenarios as opposed to

a ﬁxed target color appearance model is another

advantage of our technique over others such as

Macenko et al. (2009).

3.3

Building the Baseline

ResNet-34 (He et al., 2016) is chosen to be the

baseline for our experiment because ResNet

architecture which relies on residual connections is the

most widely used architecture and proven to be a

strong baseline among CNN architectures;

furthermore, recent development in image

classification models is getting more and more on

using the same trick of residual connections or

tweaking the original ResNet architecture (Howard

and Gugger, 2020).

3.4

Constructing Progressive Resizing

Approach

Progressive resizing approach was one of the most

important innovations when fast.ai and its team won

the DAWNBench Competition in 2018 (Howard and

Gugger, 2020). The idea is very simple, that is to start

training using small images and finally end the

training using large images. Training with small

images for most of the epochs helps finishing the

training much faster. Additionally, completing

training with large images achieves a much higher

final accuracy. Progressive resizing is also another

strategy of data augmentation. Accordingly, better

generalization of our models should be expected

when they are trained with progressive resizing.

RESULTS

4.1

The Unified Dataset

The unified dataset has three classes such as normal,

benign, and malignant.

Table 1: Statistics of image sizes which consist of mean,

standard deviation, minimum, Q1, Q2, Q3, and maximum

(measurement unit: pixel).

Statistics Width Height

Count

367

000

8, 367.000

Mean 513.540 765.802

Standard deviation 230.537 287.778

Minimum 456.000 700.000

460.000 700.000

(median)

460.000 700.000

Maximum

536

000

2, 048.000

Table 1 shows the statistics of our unified dataset.

Furthermore, 70% of the dataset is chosen randomly

to be training set and the rest is determined as

validation set. The code to unify the three datasets can

be viewed in the github repository.

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354

4.2

Normalizing Colors of Images

We employ StainTools library to do the nor-

malization. The library can be found at Peter

Byfield’s github repository

Figure 4: An image before applying Vahadane normaliza-

tion.

Figure 4 and Figure 5 show an example of an im-

age before and after normalization respectively.

Briefly, normalization is a process of replacing the

color basis of a source image with those of a

pathologist-preferred target image, while still

maintaining its original stain concentrations.

Figure 5: An image after applying Vahadane normalization.

4.3

Performance of the First Baseline

ResNet-34 model as a baseline is created by employ-

ing fast.ai library. Before the model is trained with the

unified dataset, all images are resized into the mean of

width and height of the images that is 514 pixels by 766

pixels respectively; specifically, the resizing

technique does not preserve the aspect ratio of the

images. In addition, the standard of one epoch is used

to do a fine-tuning process on the pre-trained ResNet-

3 4 ( H o w a r d a n d G u g g e r , 2 0 2 0 ) .

https://github.com/Peter554/StainTools

Table 2: The F

scores of the the baseline by fine-tuning

ResNet-34 pre-trained model (Train = train loss, Valid =

validation loss; the higher the F

score is, the better the

performance of the baseline is).

Epoch Train Valid F

score Time

0.636 0.516 85.547% 19:06

Epoch Train Valid F

score Time

0.104 0.741 83.733% 05:13

Table 2 displays the performance of ResNet-34

model. We opt to use F

score as our performance

metric since the number of instances in each class

of our dataset are imbalanced and F

is the best choice

for measuring performance on imbalanced datasets

(Sokolova and Lapalme, 2009).

4.4

Performance of the Progressive

Resizing

To assess the effect of progressive resizing, we con-

struct another baseline (ResNet-50) based on a data

augmentation technique, the so-called presizing trick.

Firstly, we resize all images to dimensions that are

significantly larger than the target training

dimensions. Next, we arrange all common

augmentation operations including a resize to the

final target size into one big chuck of operation, and

finally performing the operation on the GPU only

once at the end of trick.

The ResNet-50 enhanced with a presizing trick is

quite a strong baseline (F

score of 98.443%). How-

ever, we can still improve the performance of the

model by using the progressive resizing. Firstly, we

normalize our input data (Z-normalization) so it has a

mean of 0 and a standard deviation of 1 and verify the

effect of Z-normalization on training the model.

Table 3: The F

scores of the the second baseline (ResNet-

50) by using presizing trick (Train = train loss, Valid =

validation loss; the higher the F

score is, the better the

performance of the baseline is).

Epoch Train Valid F

score Time

0.313 0.312 91.196% 03:40

0.213 0.808 74.228% 03:39

0.160 0.089 97.547% 03:38

0.116 0.048 97.976% 03:38

0.079 0.0315 98.443% 03:38

Breast Cancer Histopathological Image Classiﬁcation using Progressive Resizing Approach

355

Table 4: F

scores of the the third baseline (ResNet-50) by

using presizing trick and Z-normalization (Train = train

loss, Valid = validation loss; the higher the F

score is, the

better the performance of the baseline is).

Epoch Train Valid F

score Time

0.578 0.376 93.182% 03:40

0.239 0.249 93.90% 03:40

0.148 0.046 98.384% 03:38

0.092 0.038 98.626% 03:39

0.072 0.037 98.682% 03:38

Table 4 shows utilizing Z-normalization improves

score a little; however, Z-normalization on input

data becomes a standard when working with pre-

trained models. Next, we employ the progressive

resizing approach by starting a training with small

images (128 pixels by 128 pixels) and ending the

training using large images (the original image size).

This approach works because features learned by early

layers of CNNs are not quite specific to the size of an

image as the layers find curves and edges. Moreover,

the subsequent layers may later find shapes such as

cell shapes. Therefore, changing image size in the

middle of the training does not mean that the

parameters of the models are completely different; it

just requires the models to learn a little bit differently,

that is by using transfer learning, in other words, fine-

tuning.

Table 5: F

scores of finding an optimal learning rate by

Cyclical Learning Rates method (Train = train loss, Valid

= validation loss; the higher the F

score is, the better the

performance of the baseline is).

Epoch Train Valid F

score Time

0.931 0.963 78.857% 03:21

0.397 0.109 95.593% 03:22

0.198 0.053 97.787% 03:21

0.115 0.042 98.426% 03:20

Table 5 displays the process of finding an

optimal learning rate by Cyclical Learning Rates

(Smith, 2017).

Table 6: F

scores of fine-tuning ResNet-50 as a part of

progressive resizing approach (Train = train loss, Valid =

validation loss; the higher the F

score is, the better the

performance of the baseline is).

Epoch Train Valid F

score Time

0.109 0.049 97.117% 03:39

Epoch Train Valid F

score Time

0.081 0.044 98.501% 03:40

0.097 0.033 98.861% 03:39

0.076 0.025 98.981% 03:38

0.060 0.025 98.924% 03:39

0.050 0.022 99.102% 03:38

Table 6 shows that utilizing progressive resizing

approach achieves F

score of 99.102%. Although our

performance cannot be compared to the current state-

of-the-art (Yan et al., 2020) and other previous works

because of different metrics (Araujo et al.,

2017;

Rakhlin et al., 2018; Golatkar et al., 2018) and dataset

(Yan et al., 2020), to the best of our knowledge, the

performance of our approach is among the highest BC

classification model considering its nearly perfect F

score. Source codes of our approach are publicly

available at our github repository.

CONCLUSIONS

We have created a unified dataset merged from three

popular datasets and propose the dataset for

advancing research in BC classification field.

Moreover, in addition to the dataset contribution, we

also provided a strong model using progressive

resizing approach whose F

score is 99.102%. We

argue that our model is comparable among other state-

of-the-art models for the dataset.

REFERENCES

Araujo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P.,

Eloy, C., Polonia, A., and Campilho, A. (2017).

“Classification of Breast Cancer Histology Images

using Convolutional Neural Networks”. PLOS ONE,

12(6), 1-14.

Aresta, G., Araujo, T., Kwok, S., Chennamsetty, S. S.,

Safwan, M., Alex, V., Marami, B., Prastawa, M., Chan,

M., Donovan, M., et al. (2019). “BACH: Grand

Challenge on Breast Cancer Histology Images”. Med.

Image Anal. 6(1), 122-139.

Asia Pacific Observatory on Health Systems and Policies

(2017). “The Republic of Indonesia Health System

ICE-TES 2021 - International Conference on Emerging Issues in Technology, Engineering, and Science

356

Review. Health Systems Tran. 7(1), 24-30.

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre,

L. A., and Jemal, A. (2018). “ Global Cancer Statistics

2018: Globocan Estimates of Incidence and Mortality

Worldwide for 36 Cancers in 185 Countries”. CA: A

Cancer J. Clin. 68(6), 394-424.

Canadian Task Force on Preventive Health Care (2011).

“Recommendations on Screening for Breast Cancer

in Average-risk Women Aged 40-74 Years”. Cmaj,

183(17), 1991-2001.

David S. Strayer, E. R. (2014). Rubin’s Pathology: Clinico-

pathologic Foundations of Medicine (Pathology (Ru-

bin)) Seventh Edition. LWW.

Gelasca, E. D., Byun, J., Obara, B., and Manjunath, B.

(2008). “Evaluation and Benchmark for Biological Im-

age Segmentation”. In 2008 15th IEEE International

Conference on Image Processing, 1816-1819. IEEE.

Geron, A. (2019). Hands-on Machine Learning with Scikit-

Learn, Keras, and TensorFlow: Concepts, Tools, and

Techniques to Build Intelligent Systems Second Edition.

O'Reilly Media Inc.

Golatkar, A., Anand, D., and Sethi, A. (2018). “Classifica-

tion of Breast Cancer Histology using Deep Learning”.

In International Conference Image Analysis and

Recognition, 837-844. Springer.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). “Deep

Residual Learning for Image Recognition”. In

Proceedings of the IEEE Conference on Computer

Vision and Pattern Recognition, 770-778.

Howard, J. and Gugger, S. (2020). Deep Learning for

Coders with fastai and PyTorch. O’Reilly Media Inc.

Institute of Medicine and National Research Council

(2005). Saving Women’s Lives: Strategies for Improving

Breast Cancer Detection and Diagnosis. The National

Academies Press, Washington, DC.

International Agency for Research on Cancer (2012). WHO

Classification of Tumours of the Breast [OP]

(Medicine) 4th Edition. World Health Organization.

Macenko, M., Niethammer, M., Marron, J. S., Borland, D.,

Woosley, J. T., Guan, X., Schmitt, C., and Thomas, N.

E. (2009). “A Method for Normalizing Histology Slides

for Quantitative Analysis”. In 2009 IEEE International

Symposium on Biomedical Imaging: From Nano to

Macro, 1107-1110. IEEE.

Marmot, M. G., Altman, D., Cameron, D., Dewar, J.,

Thompson, S., and Wilcox, M. (2013). “The Benefits

and Harms of Breast Cancer Screening: An

Independent Review”. British Journal of Cancer,

108(11), 2205.

McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J.,

Antropova, N., Ashrafian, H., Back, T., Chesus, M.,

Corrado, G. C., Darzi, A., Etemadi, M., Garcia-

Vicente, F., Gilbert, F. J., Halling-Brown, M., Hass-

abis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J.,

King, D., Ledsam, J. R., Melnick, D., Mostofi, H.,

Peng, L., Reicher, J. J., Romera-Paredes, B., Side-

bottom, R., Suleyman, M., Tse, D., Young, K. C.,

De Fauw, J., and Shetty, S. (2020). “International

Evaluation of an AI System for Breast Cancer

Screening”. Nat. 577(7788), 89-94.

Millis, R. R. (1984). “Needle Biopsy of the Breast”. Monog.

Pathol. (25), 186-203.

Rakhlin, A., Shvets, A., Iglovikov, V., and Kalinin, A. A.

(2018). “Deep Convolutional Neural Networks for

Breast Cancer Histology Image Analysis”. In

International Conference Image Analysis and

Recognition, 737-744. Springer.

Skurichina, M. and Duin, R. P. (2002). “ Bagging,

Boosting and the Random Subspace Method for Linear

Classifiers”. Pattern Anal. Appl. 5(2), 121-135.

Smith, L. N. (2017). “Cyclical Learning Rates for Training

Neural Networks”. In 2017 IEEE Winter Conference on

Applications of Computer Vision (WACV), 464-472.

IEEE.

Sokolova, M. and Lapalme, G. (2009). “A Systematic

Analysis of Performance Measures for Classification

Tasks”. Infor. Proc. Manage. 45(4), 427-437.

Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L.

(2016). “A Dataset for Breast Cancer Histopathological

Image Classification”. IEEE Trans. on Biomedical

Engineering, 63(7), 1455-1462.

Stenkvist, B., Westman-Naeser, S., Holmquist, J., Nordin,

B., Bengtsson, E., Vegelius, J., Eriksson, O., and Fox,

C. H. (1978). “Computerized Nuclear Morphometry as

an Objective Method for Characterizing Human Cancer

Cell Populations”. Cancer Res. 38(12), 4688-4697.

Tabar, L., Vitak, B., Chen, T. H.-H., Yen, A. M.-F., Cohen,

A., Tot, T., Chiu, S. Y.-H., Chen, S. L.-S., Fann, J. C.

Y., Rosell, J., et al. (2011). “Swedish Two-county Trial:

Impact of Mammographic Screening on Breast Cancer

Mortality during 3 Decades”. Radiology, 260(3), 658-

663.

Vahadane, A., Peng, T., Sethi, A., Albarqouni, S., Wang,

L., Baust, M., Steiger, K., Schlitter, A. M., Esposito, I.,

and Navab, N. (2016). “Structure-preserving Color

Normalization and Sparse Stain Separation for

Histological Images. IEEE Transactions on Medical

Imaging, 35(8), 1962-1971.

World Health Organization (2018). Data Global Can- cer

Observatory 2018. https://gco.iarc.fr/today/data/

factsheets/populations/360-indonesia-fact-sheets.pdf.

Accessed: 2020-01-04.

Yan, R., Ren, F., Wang, Z., Wang, L., Zhang, T., Liu, Y.,

Rao, X., Zheng, C., and Zhang, F. (2020). Breast Cancer

Histopathological Image Classification using a Hybrid

Deep Neural Network. Meth. 173(1), 52-60.

Zhang, Y., Zhang, B., and Lu, W. (2011). Breast Can-

cer Classification from Histological Images with

Multiple Features and Random Subspace Classifier

Ensemble. In AIP Conference Proceedings, 1371(1),

19-28.

Breast Cancer Histopathological Image Classiﬁcation using Progressive Resizing Approach

357