An Efficient Breast Cancer Segmentation System based on Deep Learning Techniques

Authors

  • Shaaban M. Shaaban Department of Electrical Engineering, College of Engineering, Northern Border University, Saudi Arabia
  • Majid Nawaz College of Sciences, Northern Border University, Saudi Arabia
  • Yahia Said Department of Electrical Engineering, College of Engineering, Northern Border University, Saudi Arabia https://orcid.org/0000-0003-0613-4037
  • Mohammad Barr Department of Electrical Engineering, College of Engineering, Northern Border University, Saudi Arabia
Volume: 13 | Issue: 6 | Pages: 12415-12422 | December 2023 | https://doi.org/10.48084/etasr.6518

Abstract

Breast cancer is one of the major threats that attack women around the world. Its detection and diagnosis in the early stages can greatly improve care efficiency and reduce mortality rate. Early detection of breast cancer allows medical professionals to use less intrusive treatments, such as lumpectomies or targeted medicines, improving survival rates and lowering morbidity. This study developed a breast cancer segmentation system based on an improved version of the U-Net 3+ neural network. Various optimizations were applied to this architecture to improve the localization and segmentation performance. An evaluation of different state-of-the-art networks was performed to improve the performance of the proposed breast cancer diagnosis system. Various experiments were carried out on the INbreast Full-Field Digital Mammographic dataset (INbreast FFDM). The results obtained demonstrated that the proposed model achieved a dice score of 98.47%, which is a new state-of-the-art segmentation finding, showcasing its efficiency in detecting breast cancer from mammography images with the possibility of implementation for real applications.

Keywords:

mammography images, deep learning, AI, dense dilated convolution, breast cancer segmentation

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References

X. Yang et al., "Deep Learning Signature Based on Staging CT for Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer," Academic Radiology, vol. 27, no. 9, pp. 1226–1233, Sep. 2020.

C. Amrit, T. Paauw, R. Aly, and M. Lavric, "Identifying child abuse through text mining and machine learning," Expert Systems with Applications, vol. 88, pp. 402–418, Dec. 2017.

K. Al-Dulaimi, V. Chandran, K. Nguyen, J. Banks, and I. Tomeo-Reyes, "Benchmarking HEp-2 specimen cells classification using linear discriminant analysis on higher order spectra features of cell shape," Pattern Recognition Letters, vol. 125, pp. 534–541, Jul. 2019.

M. Aftf, R. Ayachi, Y. Said, E. Pissaloux, and M. Atri, "Indoor Object C1assification for Autonomous Navigation Assistance Based on Deep CNN Model," in 2019 IEEE International Symposium on Measurements & Networking (M&N), Catania, Italy, Jul. 2019, pp. 1–4.

M. Afif, R. ayachi, Y. Said, E. Pissaloux, and M. Atri, "Recognizing signs and doors for Indoor Wayfinding for Blind and Visually Impaired Persons," in 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sousse, Tunisia, Sep. 2020, pp. 1–4.

Y. Said, M. Barr, and H. E. Ahmed, "Design of a Face Recognition System based on Convolutional Neural Network (CNN)," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5608–5612, Jun. 2020.

R. Ayachi, M. Afif, Y. Said, and A. B. Abdelaali, "Pedestrian detection for advanced driving assisting system: a transfer learning approach," in 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sousse, Tunisia, Sep. 2020, pp. 1–5.

R. Ayachi, M. Afif, Y. Said, and A. B. Abdelali, "Real-Time Implementation of Traffic Signs Detection and Identification Application on Graphics Processing Units," International Journal of Pattern Recognition and Artificial Intelligence, vol. 35, no. 07, Jun. 2021, Art. no. 2150024.

M. Afif, R. Ayachi, Y. Said, and M. Atri, "Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction," Multimedia Tools and Applications, vol. 82, no. 17, pp. 26885–26899, Jul. 2023.

A. M. Abdel-Zaher and A. M. Eldeib, "Breast cancer classification using deep belief networks," Expert Systems with Applications, vol. 46, pp. 139–144, Mar. 2016.

N. Behar and M. Shrivastava, "A Novel Model for Breast Cancer Detection and Classification," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9496–9502, Dec. 2022.

A. Cruz-Roa et al., "Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent," Scientific Reports, vol. 7, no. 1, Apr. 2017, Art. no. 46450.

S. J. Mambou, P. Maresova, O. Krejcar, A. Selamat, and K. Kuca, "Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model," Sensors, vol. 18, no. 9, Sep. 2018, Art. no. 2799.

M. H. Yap et al., "Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 4, pp. 1218–1226, Jul. 2018.

D. Lévy and A. Jain, "Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks." arXiv, Dec. 2016.

S. Suzuki et al., "Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis," in 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Tsukuba, Japan, Sep. 2016, pp. 1382–1386.

G. Toz and P. Erdogmus, "A Single Sided Edge Marking Method for Detecting Pectoral Muscle in Digital Mammograms," Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2367–2373, Feb. 2018.

H. Huang et al., "UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation," in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, Feb. 2020, pp. 1055–1059.

I. C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M. J. Cardoso, and J. S. Cardoso, "INbreast: Toward a Full-field Digital Mammographic Database," Academic Radiology, vol. 19, no. 2, pp. 236–248, Feb. 2012.

Z. Rezaei, "A review on image-based approaches for breast cancer detection, segmentation, and classification," Expert Systems with Applications, vol. 182, Nov. 2021, Art. no. 115204.

N. Dhungel, G. Carneiro, and A. P. Bradley, "Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms," in Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015, Munich, Germany, 2015, pp. 605–612.

M. A. Al-antari, M. A. Al-masni, M. T. Choi, S. M. Han, and T. S. Kim, "A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification," International Journal of Medical Informatics, vol. 117, pp. 44–54, Sep. 2018.

Y. D. Zhang, S. C. Satapathy, D. S. Guttery, J. M. Górriz, and S. H. Wang, "Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network," Information Processing & Management, vol. 58, no. 2, Art. no. 102439, Mar. 2021.

Y. J. Suh, J. Jung, and B.-J. Cho, "Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning," Journal of Personalized Medicine, vol. 10, no. 4, Dec. 2020, Art. no. 211.

N. Chouhan, A. Khan, J. Z. Shah, M. Hussnain, and M. W. Khan, "Deep convolutional neural network and emotional learning based breast cancer detection using digital mammography," Computers in Biology and Medicine, vol. 132, Art. no. 104318, May 2021.

D. Muduli, R. Dash, and B. Majhi, "Automated breast cancer detection in digital mammograms: A moth flame optimization based ELM approach," Biomedical Signal Processing and Control, vol. 59, May 2020, Art. no. 101912.

Y. Wang, E. J. Choi, Y. Choi, H. Zhang, G. Y. Jin, and S.-B. Ko, "Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning," Ultrasound in Medicine & Biology, vol. 46, no. 5, pp. 1119–1132, May 2020.

M. Masud, A. E. Eldin Rashed, and M. S. Hossain, "Convolutional neural network-based models for diagnosis of breast cancer," Neural Computing and Applications, vol. 34, no. 14, pp. 11383–11394, Jul. 2022.

Y. Eroğlu, M. Yildirim, and A. Çinar, "Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR," Computers in Biology and Medicine, vol. 133, Jun. 2021, Art. no. 104407.

K. Loizidou, G. Skouroumouni, C. Nikolaou, and C. Pitris, "Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms," IEEE Journal of Translational Engineering in Health and Medicine, vol. 10, pp. 1–11, 2022.

T. Nagalakshmi, "Breast Cancer Semantic Segmentation for Accurate Breast Cancer Detection with an Ensemble Deep Neural Network," Neural Processing Letters, vol. 54, no. 6, pp. 5185–5198, Dec. 2022.

S. Sharmin, T. Ahammad, Md. A. Talukder, and P. Ghose, "A Hybrid Dependable Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detection," IEEE Access, vol. 11, pp. 87694–87708, 2023.

Z. Wang, E. P. Simoncelli, and A. C. Bovik, "Multiscale structural similarity for image quality assessment," in The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, Pacific Grove, CA, USA, Aug. 2003, vol. 2, pp. 1398–1402.

A. Baccouche, B. Garcia-Zapirain, C. Castillo Olea, and A. S. Elmaghraby, "Connected-UNets: a deep learning architecture for breast mass segmentation," npj Breast Cancer, vol. 7, no. 1, pp. 1–12, Dec. 2021.

M. Alkhaleefah et al., "Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images," Cancers, vol. 14, no. 16, Jan. 2022, Art. no. 4030.

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How to Cite

[1]
S. M. Shaaban, M. Nawaz, Y. Said, and M. Barr, “An Efficient Breast Cancer Segmentation System based on Deep Learning Techniques”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 6, pp. 12415–12422, Dec. 2023.

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