Analisa Gambar X-Ray Mammography dengan Convolution Neural Network pada Deep Learning dengan Arsitektur Resnet

 (*)Nur Islamiati Sanusi Mail (Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia)
 Siti Ramadhani (Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia)
 Muhammad Irsyad (Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: June 15, 2023; Published: July 2, 2023

Abstract

Cancer is a disease that occurs when cells in the body undergo changes and grow uncontrollably. Breast cancer is one of the common types of cancer that affects women worldwide. Early detection of breast cancer is crucial to improve the survival rate. Mammography is a medical imaging method used for the early detection of breast cancer. In this context, deep learning technology and computerized classifiers, such as Convolutional Neural Network (CNN) with the Resnet model, have been used for the analysis and prediction of mammography images with promising results. Previous studies have shown high accuracy in classifying breast masses as benign or malignant using CNN and Resnet. Furthermore, CNN has also been employed for the classification of malignant and benign breast cancer, prediction of breast cancer risk, as well as detection and classification of breast masses with satisfactory accuracy rates. The use of deep learning in medical image analysis, including mammograms and X-ray images, has proven to be an effective tool in improving cancer diagnosis and treatment. The dataset used consisted of 322 images divided into 7 classes. After testing, an accuracy of 72% was achieved with a 90:10 ratio of test data to training data, along with the corresponding confusion matrix values. Therefore, it can be concluded that the Resnet method is capable of identifying breast cancer.

Keywords


Breast Cancer; Deep Learning; Resnet; CNN; Mammography

Full Text:

PDF


Article Metrics

Abstract view : 362 times
PDF - 130 times

References

K. Charan, Saira.Khan,Muhammad Jaleed.Khurshid, “Breast Cancer Detection in Mammograms using Convolutional Neural Network,” Breast Cancer Detect. Mammograms using Convolution Nural Netw., no. 978-1-5386-1370–2/18, pp. 1–5, 2018, doi: 10.1007/978-981-16-1244-2_7.

S. Z. Ramadan, “Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review,” J. Healthc. Eng., vol. 2020, 2020, doi: 10.1155/2020/9162464.

L. Tsochatzidis, L. Costaridou, and I. Pratikakis, “Deep learning for breast cancer diagnosis from mammograms — A comparative study,” J. Imaging, vol. 5, no. 3, Mar. 2019, doi: 10.3390/jimaging5030037.

P. Stiefelhagen, “Wie Sie die „Pantozolitis“ loswerden,” MMW-Fortschritte der Medizin, vol. 160, no. 10. p. 12, 2018. doi: 10.1007/s15006-018-0554-5.

S. S. M. Khairi et al., “Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis,” Healthc., vol. 10, no. 1, Jan. 2022, doi: 10.3390/healthcare10010010.

O. F. Ereken and C. Tarhan, “Breast Cancer Detection using Convolutional Neural Networks,” in ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings, Mar. 2022, pp. 597–601. doi: 10.1109/ISMSIT56059.2022.9932694.

A. N. R. Hakim, P. Prajitno, and D. S. Soejoko, “Microcalcification detection in mammography image using computer-aided detection based on convolutional neural network,” in AIP Conference Proceedings, American Institute of Physics Inc., Mar. 2021. doi: 10.1063/5.0047828.

H. Li, S. Zhuang, D. ao Li, J. Zhao, and Y. Ma, “Benign and malignant classification of mammogram images based on deep learning,” Biomed. Signal Process. Control, vol. 51, pp. 347–354, May 2019, doi: 10.1016/j.bspc.2019.02.017.

S. S. Aboutalib, A. A. Mohamed, W. A. Berg, M. L. Zuley, J. H. Sumkin, and S. Wu, “Deep learning to distinguish recalled but benign mammography images in breast cancer screening,” Clin. Cancer Res., vol. 24, no. 23, pp. 5902–5909, Dec. 2018, doi: 10.1158/1078-0432.CCR-18-1115.

S. Ramadhani and S. R. First, “A Review Comparative Mammography Image Analysis on Modified CNN Deep Learning Method,” Indones. J. Artif. Intell. Data Min., vol. 3, no. 1, pp. 1–10, 2020, doi: 10.24014/ijaidm.v2i2.xxxx.

Z. Yan, H. Liu, T. Li, J. Li, and Y. Wang, “Two dimensional correlation spectroscopy combined with ResNet: Efficient method to identify bolete species compared to traditional machine learning,” LWT, vol. 162, p. 113490, Jun. 2022, doi: 10.1016/j.lwt.2022.113490.

Z. Niswati, R. Hardatin, M. N. Muslimah, and S. N. Hasanah, “Perbandingan Arsitektur ResNet50 dan ResNet101 dalam Klasifikasi Kanker Serviks pada Citra Pap Smear,” Fakt. Exacta, vol. 14, no. 3, p. 160, Oct. 2021, doi: 10.30998/faktorexacta.v14i3.10010.

S. Showkat and S. Qureshi, “Efficacy of Transfer Learning-based ResNet models in Chest X-ray image classification for detecting COVID-19 Pneumonia,” Chemom. Intell. Lab. Syst., vol. 224, May 2022, doi: 10.1016/j.chemolab.2022.104534.

Meta AI Research, “WideResNet,” https://paperswithcode.com/model/wide-resnet?variant=wide-resnet-50-2, Feb. 2021.

D. Saranyaraj, M. Manikandan, and S. Maheswari, “A deep convolutional neural network for the early detection of breast carcinoma with respect to hyper- parameter tuning,” Multimed. Tools Appl., vol. 79, no. 15–16, pp. 11013–11038, Apr. 2020, doi: 10.1007/s11042-018-6560-x.

M. Toğaçar, B. Ergen, and Z. Cömert, “Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders,” Med. Hypotheses, vol. 135, Feb. 2020, doi: 10.1016/j.mehy.2019.109503.

K. Dembrower et al., “Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction,” Radiology, vol. 294, no. 2, pp. 265–272, 2020, doi: 10.1148/radiol.2019190872.

M. Devarakonda Venkata and S. Lingamgunta, “A Convolution Neural Network based MRI breast mass diagnosis using Zernike moments,” Mater. Today Proc., Jul. 2021, doi: 10.1016/j.matpr.2021.06.133.

D. Sarwinda, R. H. Paradisa, A. Bustamam, and P. Anggia, “Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 423–431. doi: 10.1016/j.procs.2021.01.025.

B. M. Sujatmiko, E. Yudaningtyas, and P. M. Raharjo, “Convolutional Neural Network Using ResNet Network Design As Skin Tumor Classification Method,” vol. 11, no. 1, pp. 53–64, 2022.

A. Ridhovan and A. Suharso, “PENERAPAN METODE RESIDUAL NETWORK (RESNET) DALAM KLASIFIKASI PENYAKIT PADA DAUN GANDUM,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 7, no. 1, pp. 58–65, 2022, doi: 10.29100/jipi.v7i1.2410.

Prajwal Khare, Kalpana Sharma, Sunil Dhimal, and Sital Sharma, 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP). IEEE, 2019.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Analisa Gambar X-Ray Mammography dengan Convolution Neural Network pada Deep Learning dengan Arsitektur Resnet

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Nur Islamiati Sanusi, Siti Ramadhani, Muhammad Irsyad

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

Jurnal Sistem Komputer dan Informatika (JSON)
Dikelola oleh STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email : jurnal.json@gmail.com


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