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  3. Vol. 7, No. 3, August 2022
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Vol. 7, No. 3, August 2022

Issue Published : Aug 31, 2022
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Mass Classification of Breast Cancer Using CNN and Faster R-CNN Model Comparison

https://doi.org/10.22219/kinetik.v7i3.1462
Sunardi Sunardi
Universitas Ahmad Dahlan
Anton Yudhana
Universitas Ahmad Dahlan
Anggi Rizky Windra Putri
Universitas Ahmad Dahlan

Corresponding Author(s) : Anggi Rizky Windra Putri

anggie.windra.putri@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 7, No. 3, August 2022
Article Published : Aug 30, 2022

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Abstract

Threat of breast cancer is a frightening type and threatens the female population worldwide. Early detection is preventive solution to determine cancer diagnosis or tumors in the female breast area. Today, machine learning technology in managing medical images has become an innovative trend in the health sector. This technology can accelerate diagnosing disease based on the acquisition of accuracy values. The primary purpose of this research is to innovate by comparing two deep learning models to build a prediction system for early-stage breast cancer. This research utilizes Convolutional Neural Network (CNN) sequential models and Faster Region-based Convolutional Neural Network (R-CNN) models that can determine the classification of normal or abnormal breast image data, which can determine the normal or abnormal classification of breast image. The dataset's source in this study came from the Mammographic Image Analysis Society (MIAS). This dataset consists of 322 mammogram data with 123 abnormal and 199 normal classes. The experimental results of this study show that the accuracy of the CNN and R-CNN models in image classification are 91.26% and 63.89%, respectively. Based on these results, the CNN sequential model has better accuracy than the Faster R-CNN model, because it does not require unique characteristics to detect breast cancer.

Keywords

Breast cancer Cancer R-CNN Machine learning Mammography CNN
Sunardi, S., Yudhana, A. ., & Windra Putri, A. R. (2022). Mass Classification of Breast Cancer Using CNN and Faster R-CNN Model Comparison. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 7(3), 243-250. https://doi.org/10.22219/kinetik.v7i3.1462
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References
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  26. M. C. Lailis Syafa’ah*1, Ilham Hanami2, Inda Rusdia Sofiani3, “Skin lesion Classification Using Convolutional Neural Network,” vol. 4, 2021. https://doi.org/10.3233/APC210277
  27. M. Muhammad, D. Zeebaree, A. M. A. Brifcani, J. Saeed, and D. A. Zebari, “A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images,” Journal of Applied Science and Technology Trends, vol. 1, no. 3, pp. 78–91, 2020. https://doi.org/10.38094/jastt20201328
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References


C. E. DeSantis et al., “Breast cancer statistics, 2019,” CA: A Cancer Journal for Clinicians, vol. 69, no. 6, pp. 438–451, Nov. 2019. https://doi.org/10.3322/caac.21583

A. T. Jalil, S. H. Dilfi, and A. Karevskiy, “Survey of Breast Cancer In Wasit Province , Iraq,” Global Journal of Public Health Medicine, vol. 1, no. 2, pp. 33–38, Nov. 2019. https://doi.org/10.37557/gjphm.v1i2.7

“Penyakit Kanker di Indonesia Berada Pada Urutan 8 di Asia Tenggara dan Urutan 23 di Asia – P2P Kemenkes RI,” Jan. 31, 2019.

S. K. Nur Handayani, “Kanker dan Serba-Serbinya (Hari Kanker Sedunia 2022) – RSP Respira,” Feb. 01, 2022.

T. Xie et al., “Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging,” European Radiology, vol. 29, no. 5, pp. 2535–2544, 2019. https://doi.org/10.1007/s00330-018-5804-5

D. A. Zebari, D. Q. Zeebaree, A. M. Abdulazeez, H. Haron, and H. N. A. Hamed, “Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images,” IEEE Access, vol. 8, pp. 1–20, 2020. https://doi.org/10.1109/ACCESS.2020.3036072

M. Monirujjaman Khan et al., “Machine Learning Based Comparative Analysis for Breast Cancer Prediction,” Journal of Healthcare Engineering, vol. 2022, pp. 1–15, Apr. 2022. https://doi.org/10.1155/2022/4365855

A. H. Farhan and M. Y. Kamil, “Texture Analysis of Breast Cancer via LBP, HOG, and GLCM techniques,” IOP Conference Series: Materials Science and Engineering, vol. 928, no. 7, 2020. https://doi.org/10.1088/1757-899X/928/7/072098

E. Michael, H. Ma, H. Li, and S. Qi, “An Optimized Framework for Breast Cancer Classification Using Machine Learning,” BioMed Research International, vol. 2022, pp. 1–18, Feb. 2022. https://doi.org/10.1155/2022/8482022

D. A. Ragab, M. Sharkas, and O. Attallah, “Breast cancer diagnosis using an efficient CAD system based on multiple classifiers,” Diagnostics, vol. 9, no. 4, pp. 1–26, 2019. https://doi.org/10.3390/diagnostics9040165

A. E. Minarno, M. Hazmi Cokro Mandiri, Y. Munarko, and H. Hariyady, “Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 4, 2021. https://doi.org/10.22219/kinetik.v6i2.1219

H. Jiang and E. Learned-Miller, “Face Detection with the Faster R-CNN,” in 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), May 2017, pp. 650–657. https://doi.org/10.1109/FG.2017.82

B. S. Bari et al., “A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework,” PeerJ Computer Science, vol. 7, p. e432, Apr. 2021. https://doi.org/10.7717/peerj-cs.432

U. K. Kumar, M. B. S. Nikhil, and K. Sumangali, “Prediction of breast cancer using voting classifier technique,” in 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Aug. 2017, no. August, pp. 108–114. https://doi.org/10.1109/ICSTM.2017.8089135

Z. Momenimovahed and H. Salehiniya, “Epidemiological characteristics of and risk factors for breast cancer in the world,” Breast Cancer: Targets and Therapy, vol. 11, pp. 151–164, 2019. https://doi.org/10.2147/BCTT.S176070

K. Govindaswamy and S. Ragunathan, “Genre Classification of Telugu and English Movie Based on the Hierarchical Attention Neural Network,” International Journal of Intelligent Engineering and Systems, vol. 14, no. 1, pp. 54–62, 2021. https://doi.org/10.22266/ijies2021.0228.06

C. Orozco and C. Rebong, “Vehicular Detection and Classification for Intelligent Transportation System: A Deep Learning Approach Using Faster R-CNN Model,” International journal of simulation: systems, science & technology, pp. 1–7, Jul. 2019. https://doi.org/10.5013/IJSSST.a.20.S2.11

S. Cahyaningtyas, D. Hatta Fudholi, and A. Fathan Hidayatullah, “Deep Learning for Aspect-Based Sentiment Analysis on Indonesian Hotels Reviews,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 4, no. 3, 2021. https://doi.org/10.22219/kinetik.v6i3.1300

W. Setiawan, A. Ghofur, F. Hastarita Rachman, and R. Rulaningtyas, “Deep Convolutional Neural Network AlexNet and Squeezenet for Maize Leaf Diseases Image Classification,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 4, pp. 0–7, 2021. https://doi.org/10.22219/kinetik.v6i4.1335

A. Peryanto, A. Yudhana, and R. Umar, “Rancang Bangun Klasifikasi Citra Dengan Teknologi Deep Learning Berbasis Metode Convolutional Neural Network,” Format : Jurnal Ilmiah Teknik Informatika, vol. 8, no. 2, p. 138, Feb. 2020. https://dx.doi.org/10.22441/format.2019.v8.i2.007

W. P. Sari and H. Fahmi, “Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 4, no. 3, 2021. https://doi.org/10.22219/kinetik.v6i3.1272

H.-P. Chan, R. K. Samala, and L. M. Hadjiiski, “CAD and AI for breast cancer—recent development and challenges,” The British Journal of Radiology, vol. 93, no. 1108, p. 20190580, Apr. 2020. https://doi.org/10.1259/bjr.20190580

A. Peryanto, A. Yudhana, and R. Umar, “Convolutional Neural Network and Support Vector Machine in Classification of Flower Images,” Khazanah Informatika: Jurnal …, vol. 8, no. 1, pp. 1–7, 2022. https://doi.org/10.23917/khif.v8i1.15531

M. F. Rahman and B. Bambang, “Deteksi Sampah pada Real-time Video Menggunakan Metode Faster R-CNN,” Applied Technology and Computing Science Journal, vol. 3, no. 2, pp. 117–125, Mar. 2021. https://doi.org/10.33086/atcsj.v3i2.1846

A. F. Fadhil and H. K. Ornek, “A Computer-Aided Detection System for Breast Cancer Detection and Classification,” Selcuk University Journal of Engineering Sciences, vol. 20, no. 1, pp. 23–31, 2021.

M. C. Lailis Syafa’ah*1, Ilham Hanami2, Inda Rusdia Sofiani3, “Skin lesion Classification Using Convolutional Neural Network,” vol. 4, 2021. https://doi.org/10.3233/APC210277

M. Muhammad, D. Zeebaree, A. M. A. Brifcani, J. Saeed, and D. A. Zebari, “A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images,” Journal of Applied Science and Technology Trends, vol. 1, no. 3, pp. 78–91, 2020. https://doi.org/10.38094/jastt20201328

A. A. Wahab, M. I. M. Salim, J. Yunus, and M. H. Ramlee, “Comparative evaluation of medical thermal image enhancement techniques for breast cancer detection,” Journal of Engineering and Technological Sciences, vol. 50, no. 1, pp. 40–52, 2018. https://dx.doi.org/10.5614/j.eng.technol.sci.2018.50.1.3

M. S. Dawngliani, N. Chandrasekaran, R. Lalmawipuii, and H. Thangkhanhau, “Breast Cancer Recurrence Prediction Model Using Voting Technique,” in EAI/Springer Innovations in Communication and Computing, 2021, pp. 17–28. https://doi.org/10.1007/978-3-030-49795-8_2

J. R. Marsilin, “An Efficient CBIR Approach for Diagnosing the Stages of Breast Cancer Using KNN Classifier,” Bonfring International Journal of Advances in Image Processing, vol. 2, no. 1, pp. 01–05, Mar. 2012. http://dx.doi.org/10.9756/BIJAIP.1127

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