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Mass Classification of Breast Cancer Using CNN and Faster R-CNN Model Comparison
Corresponding Author(s) : Anggi Rizky Windra Putri
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 7, No. 3, August 2022
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.
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- 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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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
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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
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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
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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