Breast Cancer Detection Using Image Denoising and UNet Segmentation for Mammography Images
Keywords:
Breast Cancer, DenseNet, Mammography, NLW, VGG19, WaterUNetAbstract
Breast cancer remains a significant global health concern, necessitating the development of effective risk assessment for detection and prevention. It is a worldwide health issue that requires enhanced early detection methods. In this paper represents breast cancer detection technique using advanced learning techniques. Initially the dataset has collected form benchmark datasets as Mammography with Benign Malignant Masses with three categories like MIAS, Inbreast and DDSM. The Dataset has trained using VGG19 with Modified Densenet. The Image Denoising has used Non local mean with Wavelet (NLW) Algorithm. NLW denoising minimizes noise and preserves features, guaranteeing high-quality images for analysis. Image segmentation has done using WaterUNet model. WaterUNet model is correctly identifies malignant spots in mammography images. Finally, the classification has done with VGG19 algorithm with 99% accuracy. Experimental results show that the proposed technique outperforms with existing approaches like accuracy, precision, recall and f-measure.
Downloads
References
A.Intasam et al., "Optimizing the Hyperparameter Tuning of YOLOv5 for Breast Cancer Detection," 2023 9th International Conference on Engineering, Applied Sciences, and Technology (ICEAST), Vientiane, Laos, 2023, pp. 184-187, doi: 10.1109/ICEAST58324.2023.10157611.
A.Sinibaldi et al., "Direct Competitive Assay for ERBB2 Detection in Breast Cancer Cell Lysates Using 1-D Photonic Crystals-Based Biochips," in IEEE Sensors Letters, vol. 7, no. 8, pp. 1-4, Aug. 2023, Art no. 4501204, doi: 10.1109/LSENS.2023.3297372.
D. Alsaedi, A. Melniko, K. Muzaffar, A. Mandelis and O. M. Ramahi, "A Microwave-Thermography-Convolution Neural Network Technique for Breast Cancer Detection," 2021 IEEE Asia-Pacific Conference on Applied Electromagnetics (APACE), Penang, Malaysia, 2021, pp. 1-2, doi: 10.1109/APACE53143.2021.9760519.
D. Deepa, M. S. Raj, S. Gowthami, K. Hemalatha, C. Poongodi and P. Thangavel, "Identification and Analysis of Alzheimer’s Disease using DenseNet Architecture with Minimum Path Length Between Input and Output Layers," 2022 Smart Technologies, Communication and Robotics (STCR), Sathyamangalam, India, 2022, pp. 1-5, doi: 10.1109/STCR55312.2022.10009552.
D. Fajri Riesaputri, C. Atika Sari, I. M. S. De Rosal and E. Hari Rachmawanto, "Classification of Breast Cancer using PNN Classifier based on GLCM Feature Extraction and GMM Segmentation," 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia, 2020, pp. 83-87, doi: 10.1109/iSemantic50169.2020.9234207.
D. Kusumawati, A. A. Ilham, A. Achmad and I. Nurtanio, "Vgg-16 And Vgg-19 Architecture Models In Lie Detection Using Image Processing," 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, 2022, pp. 340-345, doi: 10.1109/ICITISEE57756.2022.10057748.
G. Hamed, M. Marey, S. E. -S. Amin and M. F. Tolba, "A Proposed Model for Denoising Breast Mammogram Images," 2018 13th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, 2018, pp. 652-657, doi: 10.1109/ICCES.2018.8639307.
G. Latif, M. O. Butt, F. Yousif Al Anezi and J. Alghazo, "Ultrasound Image Despeckling and detection of Breast Cancer using Deep CNN," 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), Ho Chi Minh City, Vietnam, 2020, pp. 1-5, doi: 10.1109/RIVF48685.2020.9140767.
H. M. Basha and G. Sindhu, "Improved Accuracy of Early Stage Breast Cancer Detection using Anisotropic Diffusion Algorithm And Variational Partial Differential Equation Method," 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2022, pp. 1683-1689, doi: 10.1109/ICSCDS53736.2022.9760736.
K. P. Adila and K. Sheeba, "Feature fused breast cancer detection," 2020 International Conference on Futuristic Technologies in Control Systems & Renewable Energy (ICFCR), Malappuram, India, 2020, pp. 1-5, doi: 10.1109/ICFCR50903.2020.9249995.
M. Slimi, B. Jmai, P. Mendes and A. Gharsallah, "Breast cancer detection based on CPW antenna," 2019 IEEE 19th Mediterranean Microwave Symposium (MMS), Hammamet, Tunisia, 2019, pp. 1-4, doi: 10.1109/MMS48040.2019.9157301.
M. U. Hoque, T. M. Shahriar Sazzad, A. K. M. A. Farabi, I. Hosen and M. A. Somi, "An Automated Approach to Detect Breast Cancer Tissue Using Ultrasound Images," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1-4, doi: 10.1109/ICASERT.2019.8934546.
M. Wang, J. Wu, D. Xu and S. Mao, "Optimization of Parallel Stochastic Gradient Descent on Sunway TaihuLight," 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 2020, pp. 18-23, doi: 10.1109/MLBDBI51377.2020.00010.
N. Aziza and F. Samad, "Early Breast Cancer Detection with Microwave Imaging Technique by Using Spiral PIFA Antenna," 2021 5th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 2021, pp. 1-5, doi: 10.1109/EICT54103.2021.9733569.
N. F. Razali, I. S. Isa, S. N. Sulaiman, N. K. A. Karim and M. K. Osman, "High-level Features in Deeper Deep Learning Layers for Breast Cancer Classification," 2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, 2021, pp. 170-175, doi: 10.1109/ICCSCE52189.2021.9530911.
P. E. Jebarani, N. Umadevi, H. Dang and M. Pomplun, "A Novel Hybrid K-Means and GMM Machine Learning Model for Breast Cancer Detection," in IEEE Access, vol. 9, pp. 146153-146162, 2021, doi: 10.1109/ACCESS.2021.3123425.
P. K. Samanta, N. K. Rout and G. Panda, "A novel deep CNN model for improved breast cancer detection using ultrasound images," 2023 International Conference on Communication, Circuits, and Systems (IC3S), BHUBANESWAR, India, 2023, pp. 1-4, doi: 10.1109/IC3S57698.2023.10169383.
S. Momtahen, M. Momtahen, R. Ramaseshan and F. Golnaraghi, "A Machine Learning Approach: NIR Scattering Data Analysis for Breast Cancer Detection and Classification," 2022 IEEE 1st Industrial Electronics Society Annual On-Line Conference (ONCON), kharagpur, India, 2022, pp. 1-6, doi: 10.1109/ONCON56984.2022.10127055.
S. Zhang and L. Wang, "An improved non-local means filter for image mixed noise," 2021 China Automation Congress (CAC), Beijing, China, 2021, pp. 5545-5548, doi: 10.1109/CAC53003.2021.9727520.
T. Gayathri, T. Madhavi and K. R. Kumari, "A Prediction of breast cancer based on Mayfly Optimized CNN," 2022 International Conference on Computing, Communication and Power Technology (IC3P), Visakhapatnam, India, 2022, pp. 176-180, doi: 10.1109/IC3P52835.2022.00044.
U. Khasana, R. Sigit and H. Yuniarti, "Segmentation of Breast Using Ultrasound Image for Detection Breast Cancer," 2020 International Electronics Symposium (IES), Surabaya, Indonesia, 2020, pp. 584-587, doi: 10.1109/IES50839.2020.9231629.
Y. Feng, X. Chen, L. Lilge, K. Zhang, B. Su and C. Lin, "Feasibility Analysis of Spectral Detection of Breast Cancer Based on Monte Carlo Method," 2022 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO), Tianjin, China, 2022, pp. 368-372, doi: 10.1109/3M-NANO56083.2022.9941659.
Z. Aizaz, K. Khare, A. Khursheed and A. Tirmizi, "Pix2Pix Generative adversarial Networks (GAN) for breast cancer detection," 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), Aligarh, India, 2022, pp. 1-5, doi: 10.1109/IMPACT55510.2022.10029087.
Z. KHOMSI, A. ELOUERGHI, A. AFYF and L. BELLARBI, "Contribution for the Early Detection of Breast Cancer by a Superficial Thermography Solution," 2020 International Conference on Electrical and Information Technologies (ICEIT), Rabat, Morocco, 2020, pp. 1-4, doi: 10.1109/ICEIT48248.2020.9113225.
M. Robin, J. John and A. Ravikumar, "Breast Tumor Segmentation using U-NET," 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2021, pp. 1164-1167, doi: 10.1109/ICCMC51019.2021.9418447.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
