Document Type : Applied Article
Authors
Computer Science Department, University of Jember, Indonesia.
Abstract
Breast cancer is a disease of abnormal cell proliferation in the breast tissue organs. One method for diagnosing and screening breast cancer is mammography. However, the results of this mammography image have limitations because it has low contrast and high noise and contrast as non-coherence. This research segmented breast cancer images derived from Ultrasonography (USG) photo using a Convolutional Neural Network (CNN) using the U-Net architecture. Testing on the CNN model with the U-Net architecture results the highest Mean Intersection over Union (Mean IoU) value in the data scenario with a ratio of 70:30, 100 epochs, and a learning rate of 5x10-5, which is 77%, while the lowest Mean IoU in the data scenario with a ratio 90:10, 50 epochs, and a learning rate of 1x10-4 learning rate, which is 64.4%.
Keywords
Main Subjects
[1] Y. Feng et al., “Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis,” Genes Dis., vol. 5, no. 2, pp. 77–106, 2018, doi: 10.1016/j.gendis.2018.05.001.
[2] K. Unger-Saldaña, “Challenges to the early diagnosis and treatment of breast cancer in developing countries,” World J. Clin. Oncol., vol. 5, no. 3, pp. 465–477, 2014, doi: 10.5306/wjco.v5.i3.465.
[3] S. Kumar and G. S. Preetha, “Health promotion: An effective tool for global health,” Indian J. Community Med., vol. 37, no. 1, pp. 5–12, 2012, doi: 10.4103/0970-0218.94009.
[4] L. Heck and J. Herzen, “Recent advances in X-ray imaging of breast tissue: From two- to three-dimensional imaging,” Phys. Medica, vol. 79, no. September, pp. 69–79, 2020, doi: 10.1016/j.ejmp.2020.10.025.
[5] K. Loizidou, G. Skouroumouni, C. Nikolaou, and C. Pitris, “A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms,” Tomogr. (Ann Arbor, Mich.), vol. 8, no. 6, pp. 2874–2892, 2022, doi: 10.3390/tomography8060241.
[6] T. S. Menes, D. Coster, D. Coster, and S. Shenhar-Tsarfaty, “Contribution of clinical breast exam to cancer detection in women participating in a modern screening program,” BMC Womens. Health, vol. 21, no. 1, pp. 1–8, 2021, doi: 10.1186/s12905-021-01507-x.
[7] L. Fakharzadeh, S. J. Hosseini, N. N. AKBARI, S. Khazni, and ..., “The Effect of Employing Clinical Teaching Associate Model on Cognitive Learning and Clinical Skills of Nursing Students,” Int. J. Sci. Study, No. January 2018, 2017, doi: 10.17354/ijssNov/2017/51.
[8] H. Aljuaid, N. Alturki, N. Alsubaie, L. Cavallaro, and A. Liotta, “Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning,” Comput. Methods Programs Biomed., vol. 223, p. 106951, 2022, doi: 10.1016/j.cmpb.2022.106951.
[9] D. A. Zebari et al., “Systematic Review of Computing Approaches for Breast Cancer Detection Based Computer Aided Diagnosis Using Mammogram Images,” Appl. Artif. Intell., vol. 35, no. 15, pp. 2157–2203, 2021, doi: 10.1080/08839514.2021.2001177.
[10] A. A. Almazloum, A. R. Al-Hinnawi, R. De Fazio, and P. Visconti, “Assessment of Multi-Layer Perceptron Neural Network for Pulmonary Function Test’s Diagnosis Using ATS and ERS Respiratory Standard Parameters,” Computers, vol. 11, no. 9, 2022, doi: 10.3390/computers11090130.
[11] E. Mahoro and M. A. Akhloufi, “Applying Deep Learning for Breast Cancer Detection in Radiology,” Curr. Oncol., vol. 29, no. 11, pp. 8767–8793, 2022, doi: 10.3390/curroncol29110690.
[12] A. Patil and M. Rane, “Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition,” Smart Innov. Syst. Technol., vol. 195, pp. 21–30, 2021, doi: 10.1007/978-981-15-7078-0_3.
[13] O. Prentkovskis, J. Brezina, I. Konovalenko, P. Maruschak and J. Brezinová, “Research of U-Net-Based CNN Architectures for Metal Surface Defect Detection” Machines, vol. 251, no. 2, p. 24, 2022, doi: 10.1201/b15870-17.
[14] S. Bukhori, S. D. Maysaroha, and J. Adi, “Classification of Breast Cancer Tumors using a Random Forest on Mammogram Images,” vol. 45, no. 1, pp. 6–17, 2023.
[15] R. Ramadan and S. Aly, “CU-Net: A New Improved Multi-Input Color U-Net Model for Skin Lesion Semantic Segmentation,” IEEE Access, vol. 10, pp. 15539–15564, 2022, doi: 10.1109/ACCESS.2022.3148402.
[16] T. Falk et al., “U-Net: deep learning for cell counting, detection, and morphometry,” Nat. Methods, vol. 16, no. 1, pp. 67–70, 2019, doi: 10.1038/s41592-018-0261-2.
[17] A. Huang, L. Jiang, J. Zhang, and Q. Wang, “Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images,” Quant. Imaging Med. Surg., vol. 12, no. 6, pp. 3138–3150, 2022, doi: 10.21037/qims-21-1074.
[18] A. Hashemi Fath, F. Madanifar, and M. Abbasi, “Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems,” Petroleum, vol. 6, no. 1. pp. 80–91, 2020. doi: 10.1016/j.petlm.2018.12.002.
[19] C. L. Fan and Y. J. Chung, “Design and Optimization of CNN Architecture to Identify the Types of Damage Imagery,” Mathematics, vol. 10, no. 19, 2022, doi: 10.3390/math10193483.
[20] Y. Yu et al., “Techniques and Challenges of Image Segmentation: A Review,” Electron., vol. 12, no. 5, 2023, doi: 10.3390/electronics12051199.
[21] M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation,” J. Med. Imaging, vol. 6, no. 1, pp. 014006-1-014006–16, 2019, doi: 10.1117/1.JMI.6.1.014006.
[22] X. X. Yin, L. Sun, Y. Fu, R. Lu, and Y. Zhang, “U-Net-Based Medical Image Segmentation,” J. Healthc. Eng., vol. 2022, 2022, doi: 10.1155/2022/4189781.
[23] E. Govinda and V. BSSrilatha Indira Dutt, “Breast Cancer Detection Using UWB Imaging and Convolutional Neural Network,” Ilkogr. Online-Elementary Educ. Online, Year, vol. 20, no. 5, pp. 1657–1670, 2021, doi: 10.17051/ilkonline.2021.05.181.
[24] X. Zhang et al., “Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks,” IEEE Trans. Nanobioscience, vol. 17, no. 3, pp. 237–242, 2018, doi: 10.1109/TNB.2018.2845103.
[25] M. H. Yap et al., “Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks,” IEEE J. Biomed. Heal. Informatics, vol. 22, no. 4, pp. 1218–1226, 2018, doi: 10.1109/JBHI.2017.2731873.
[26] R. Rouhi, M. Jafari, S. Kasaei, and P. Keshavarzian, “Benign and malignant breast tumors classification based on region growing and CNN segmentation,” Expert Syst. Appl., vol. 42, no. 3, pp. 990–1002, 2015, doi: 10.1016/j.eswa.2014.09.020.
[27] G. Kom, A. Tiedeu, and M. Kom, “Automated detection of masses in mammograms by local adaptive thresholding,” Comput. Biol. Med., vol. 37, no. 1, pp. 37–48, 2007, doi: 10.1016/j.compbiomed.2005.12.004.
[28] D. C. Pereira, R. P. Ramos, and M. Z. do Nascimento, “Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm,” Comput. Methods Programs Biomed., vol. 114, no. 1, pp. 88–101, 2014, doi: 10.1016/j.cmpb.2014.01.014.
[29] W. Al-Dhabyani, G. Gomaa, H. Khaled and A. Fahmy, “Dataset of breast ultrasound images,” Data Brief., 2020, doi: 10.1016/j.dib.2019.104863.
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