Automated Detection of Breast Cancer Histopathology Image Using Convolutional Neural Network and Transfer Learning
Keywords:
BreakHis, Breast cancer, Convolutional Neural Networks, Residual Neural Network, Visual Geometry Group
Abstract
cancer caused 2.3 million cases and 685,000 deaths in 2020. Histopathology analysis is one of the tests used to determine a patient’s prognosis. However, histopathology analysis is a time-consuming and stressful process. With advances in deep learning methods, computer vision science can be used to detect cancer in medical images, which is expected to improve the accuracy of prognosis. This study aimed to apply Convolutional Neural Network (CNN) and Transfer Learning methods to classify breast cancer histopathology images to diagnose breast tumors. This method used CNN, Transfer Learning ((Visual Geometry Group (VGG16), and Residual Network (ResNet50)). These models undergo data augmentation and balancing techniques applied to undersampling techniques. The dataset used for this study was ”The BreakHis Database of microscopic biopsy images of breast tumors (benign and malignant),” with 1693 data classified into two categories: Benign and Malignant. The results of this study were based on recall, precision, and accuracy values. CNN accuracy was 94%, VGG16 accuracy was 88%, and ResNet50 accuracy was 72%. The conclusion was that the CNN method is recommended in detecting breast cancer to diagnose breast cancer.
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[2] M. Arnold, E. Morgan, H. Rumgay, A. Mafra, D. Singh, M. Laversanne, J. Vignat, J. R. Gralow, F. Cardoso, S. Siesling, and
I. Soerjomataram, “Current and Future Burden of Breast Cancer: Global Statistics for 2020 and 2040,” The Breast, vol. 66, no.
September, pp. 15–23, dec 2022.
[3] H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global Cancer Statistics 2020:
GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for
Clinicians, vol. 71, no. 3, pp. 209–249, 2021.
[4] A. Erickson, M. He, E. Berglund, M. Marklund, R. Mirzazadeh, N. Schultz, L. Kvastad, A. Andersson, L. Bergenstr°ahle,
J. Bergenstr°ahle, L. Larsson, L. Alonso Galicia, A. Shamikh, E. Basmaci, T. D´ıaz De St°ahl, T. Rajakumar, D. Doultsinos,
K. Thrane, A. L. Ji, P. A. Khavari, F. Tarish, A. Tanoglidi, J. Maaskola, R. Colling, T. Mirtti, F. C. Hamdy, D. J. Woodcock,
T. Helleday, I. G. Mills, A. D. Lamb, and J. Lundeberg, “Spatially Resolved Clonal Copy Number Alterations in Benign and
Malignant Tissue,” Nature, vol. 608, no. 7922, pp. 360–367, aug 2022.
[5] J. Qi, A. Gao, X. Ma, Y. Song, G. Zhao, J. Bai, E. Gao, K. Zhao, B. Wen, Y. Zhang, and J. Cheng, “Differentiation of Benign
From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram,” Frontiers in Oncology,
vol. 12, no. July, pp. 1–12, 2022.
[6] G. O’Dowd, S. Bell, and S. Wright, Wheater’s Pathology: A Text, Atlas and Review of Histopathology E-Book, ser. Wheater’s
Histology and Pathology. Elsevier Health Sciences, 2019.
[7] A. Yusnina, I. Muhimmah, and I. Fidianingsih, “Ekstraksi Fitur untuk Sel Abnormal Pleomorfik pada Kanker Payudara,” Automata,
vol. 2, no. 2, pp. 73–79, 2021.
[8] F. P. Chatamy, N. Hasanah, and H. Irawirawan, “Implementasi Metode CNN Multi-Scale Input dan Multi-Feature Network
untuk Dugaan Kanker Payudara,” Jurnal Sains dan Kesehatan, vol. 4, no. 2, pp. 126–131, 2022.
[9] R. Amini, I. Muhimmah, and I. Fidianingsih, “Analisis Fitur untuk Grading Abnormal Sel Mitosis Pada Kasus Kanker Payudara,”
Automata, vol. 2, no. 1, pp. 3–7, 2021.
[10] C. Zhu, F. Song, Y. Wang, H. Dong, Y. Guo, and J. Liu, “Breast cancer histopathology image classification through assembling
multiple compact CNNs,” BMC Medical Informatics and Decision Making, vol. 19, no. 1, pp. 1–17, 2019.
[11] X. Zhang, J. Zhou, W. Sun, and S. K. Jha, “A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis,”
Computers, Materials and Continua, vol. 72, no. 1, pp. 1123–1137, 2022.
[12] M. A. Haq, “CNN Based Automated Weed Detection System Using UAV Imagery,” Computer Systems Science and Engineering,
vol. 42, no. 2, pp. 837–849, 2021.
[13] S. Balasundaram, R. Balasundaram, G. Rasuthevar, C. Joseph, A. G. Vimala, N. Rajendiran, and B. Kaliyamurthy, “Automated
detection and classification of breast cancer nuclei with deep convolutional neural network,” Journal of ICT Research and
Applications, vol. 15, no. 2, pp. 139–151, 2021.
[14] S. A. Alanazi, M. M. Kamruzzaman, M. N. Islam Sarker, M. Alruwaili, Y. Alhwaiti, N. Alshammari, and M. H. Siddiqi,
“Boosting Breast Cancer Detection Using Convolutional Neural Network,” Journal of Healthcare Engineering, vol. 2021, no.
April, pp. 1–11, apr 2021.
[15] M. L. R. Filho and O. A. C. Cortes, “Efficient Breast Cancer Classification Using Histopathological Images and a Simpler
VGG,” Revista de Informatica Teorica e Aplicada, vol. 29, no. 1, pp. 102–114, 2022.
[16] Jalu Nusantoro, Faldo Fajri Afrinanto, Wana Salam Labibah, Zamah Sari, and Yufis Azhar, “Detection of Covid-19 on X-Ray
Image of Human Chest Using CNN and Transfer Learning,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6,
no. 3, pp. 430–441, 2022.
[17] K. Muzaki, “Breast Cancer Images on Histopathology Slides,” 2020.
[18] T. A. Zuraiyah, S. Maryana, and A. Kohar, “Automatic Door Access Model Based on Face Recognition using Convolutional
Neural Networ,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 22, no. 1, pp. 241–252, 2022.
[19] S. Tammina, “Transfer Learning Using VGG-16 with Deep Convolutional Neural Network for Classifying Images,” International
Journal of Scientific and Research Publications (IJSRP), vol. 9, no. 10, pp. 143–150, oct 2019.
[20] N. M. Blauch, M. Behrmann, and D. C. Plaut, “Computational Insights into Human Perceptual Expertise for Familiar and
Unfamiliar Face Recognition,” Cognition, vol. 208, no. March, pp. 1–13, mar 2021.
[21] Faiz Nashrullah, Suryo Adhi Wibowo, and Gelar Budiman, “The Investigation of Epoch Parameters in ResNet-50 Architecture
for Pornographic Classification,” Journal of Computer, Electronic, and Telecommunication, vol. 1, no. 1, pp. 1–8, 2020.
[22] Q. Ji, J. Huang, W. He, and Y. Sun, “Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases
from Optical Coherence Tomography Images,” Algorithms, vol. 12, no. 3, pp. 1–12, 2019.
[23] A. Anton, N. F. Nissa, A. Janiati, N. Cahya, and P. Astuti, “Application of Deep Learning Using Convolutional Neural Network
(CNN) Method For Women’s Skin Classification,” Scientific Journal of Informatics, vol. 8, no. 1, pp. 144–153, 202.
[24] R. Prathivi, “The Optimization of the Convolutional Neural Network Transfer Learning Model for CIFAR-10 Image Classification,”
Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi),, vol. 4, no. 4, pp. 717–722, 2020.
Published
2023-07-03
How to Cite
Chandranegara, D., Pratama, F., Fajrianur, S., Putra, M., & Sari, Z. (2023). Automated Detection of Breast Cancer Histopathology Image Using Convolutional Neural Network and Transfer Learning. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(3), 455-468. https://doi.org/https://doi.org/10.30812/matrik.v22i3.2803
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