Breast Cancer Detection in Mammography Image using Convolutional Neural Network

Farrel Fahrozi; Sugondo Hadiyoso; Yuli Sun Hariyani

doi:10.17529/jre.v18i1.23255

Breast Cancer Detection in Mammography Image using Convolutional Neural Network

Farrel Fahrozi, Sugondo Hadiyoso, Yuli Sun Hariyani

Abstract

Breast cancer is one of the non-contagious diseases that tends to increase every year. This disease occurs almost entirely in women, but can also occur in men. One way to detect this disease is by observing mammography images. However, mammography images often tend to be blurry with low quality so that it is possible to detect them incorrectly. Therefore, in this study, automatic classification of breast cancer on mammographic images was carried out using the Convolutional Neural Network (CNN). This proposed system uses the VGG16 architecture with a transfer learning system. The proposed system is then optimized using Adam optimizers and RMSprop optimizers. The results of system testing for normal, benign, and malignant classifications obtained an accuracy value of 80% - 90% with the highest accuracy achieved using Adam's optimizers. With this proposed system, it is hoped that it can help in the clinical diagnosis of breast cancer.

Keywords

breast cancer; mammography; CNN; classification

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References

The Global Cancer Observatory, “Cancer Incident in Indonesia,”2020.

X. Chen et al., “Non-invasive early detection of cancer four years before conventional diagnosis using a blood test,” Nat. Commun., vol. 11, no. 1, pp. 1–10, 2020.

M. Funke, “Bildgebende diagnostik des dammakarzinoms: ein update,” Radiologe, vol. 56, no. 10, pp. 921–938, 2016.

P. Gede and S. Cipta, “Prediksi citra makanan menggunakan convolutional neural network untuk menentukan besaran kalori makanan,” J. Teknol. Inf. dan Komput., vol. 6, no. 1, pp. 30–38, 2020.

A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi citra menggunakan convolutional neural network dan K-fold cross validation,” J. Appl. Informatics Comput., vol. 4, no. 1, pp. 45–51, 2020.

I. B. L. M. Suta, R. S. Hartati, and Y. Divayana, “Diagnosa tumor otak berdasarkan citra MRI (Magnetic Resonance Imaging),” Maj. Ilm. Teknol. Elektro, vol. 18, no. 2, 2019.

A. Amaliah and I. Puspita, “Deteksi lokasi tumor payudara menggunakan algoritma morfologi dan multilevel threshold,” J. Keteknikan dan Sains, vol. 1, no. 2, pp. 63–68, 2018.

G. Litjens, T. Kooi, B. E. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol 42, pp. 60–88, 2017.

B. Sahiner, A. Pezeshk, L. M. Hadjiiski, X. Wang, K. Drukker, K. H. Cha KH, “Deep learning in medical imaging and radiation therapy”, Medical Physics, vol. 46, no 1, pp. e1–e36, 2019.

M. Ahmad, M. J. Khan, A. Yousaf, S. Ghuffar, K. Khurshid, “Deep Learning: A breakthrough in medical imaging,” Current Medical Imaging Reviews. Vol. 16, no. 8, pp. 946-956, 2020.

L. Tobias, A. Ducournau, F. Rousseau, G. Mercier, and R. Fablet, “Convolutional Neural Networks for object recognition on mobile devices: A case study,” in Proceedings - International Conference on Pattern Recognition, 2016, pp. 3530–3535.

W. Al-Dhabyani, A. Fahmy, M. Gomaa, and H. Khaled, “Deep learning approaches for data augmentation and classification of breast masses using ultrasound images,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 5, pp. 618–627, 2019.

M. Gao, J. Chen, H. Mu, and D. Qi, “A transfer residual neural network based on resnet-34 for detection of wood knot defects,”Forests, vol. 12, no. 2, pp. 1–16, 2021.

T. Pandit, A. Kapoor, R. Shah, and R. Bhuva, “Understanding inception network architecture for image classification,”Experiments For Architectural Basis of Convolutional Neural Networks For Image Recognition, Tech. Report, February 2020.

S. K. Venu, “An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images,” in ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence, 2021, vol. 2, pp. 390–401.

D. Sinha and M. El-Sharkawy, “Thin MobileNet: An Enhanced MobileNet Architecture,” in 2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019, 2019, pp. 0280–0285.

M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in 36th International Conference on Machine Learning, ICML 2019, 2019, vol. 2019-June, pp. 10691–10700.

F. Yilmaz and A. Demir, “Cutting Effect on classification using nasnet architecture,” in TIPTEKNO 2020 - Tip Teknolojileri Kongresi - 2020 Medical Technologies Congress, TIPTEKNO 2020, 2020, pp. 1–3.

V. H. Phung and E. J. Rhee, “A High-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets,” Appl. Sci., vol. 9, no. 21, pp. 1–16, 2019.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations, ICLR 2015 – Conference Track Proceedings, 2015, pp. 1–14.

K. Novar Setiawan and I. M. Suwija Putra, “Klasifikasi citra mammogram menggunakan metode K-Means, GLCM, dan Support Vector Machine (SVM),” J. Ilm. Merpati (Menara Penelit. Akad. Teknol. Informasi), vol. 6, no. 1, pp. 13, 2018.

M. Fuad and W. Setiawan, “Klasifikasi kanker payudara menggunakan ekstraksi ciri metode statistik,” Multinetics, vol. 2, no. 2, pp. 54–58, 2016.

L. M. Wisudawati, “Klasifikasi tumor jinak dan tumor ganas pada citra mammogram menggunakan gray level co-cccurrence matrix (GLCM) dan Support Vector Machine (SVM),” J. Ilm. Inform. Komput., vol. 26, no. 2, pp. 176–186, 2021.

DOI: https://doi.org/10.17529/jre.v18i1.23255

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