Enhancing Breast Cancer Detection in Mammography with UNet++ - Deep Learning Approach
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Abstract
Purpose: Manually segmenting mammograms is time-consuming and subjective. Hence, developing an automatic method to address challenges like low signal-to-noise ratio, various mass shapes and sizes, and high false positive rates is crucial. In this study, we present an automated approach for mass segmentation to address these challenges effectively.
Materials and Methods: Our proposed system consists of two phases: the pre-processing phase, which includes denoising, contrast enhancement, image cropping, resizing, and augmentation of mammograms; and the model design phase, where UNet++ is employed as an encoder-decoder-based network for segmenting breast masses. The encoder captures relevant information from various regions in the input image, while the decoder reconstructs the spatial location of the target region. We extensively experimented with a publicly accessible CBIS-DDSM dataset to evaluate our proposed system performance.
Results: Based on our findings, our proposed method demonstrates promising results with a precision rate of 91.84%, a True Positive Rate of rate of 93.66%, a Dice Score Coefficient measuring 92.66%, and a Jaccard Index of 86.46%.
Conclusion: The use of UNet++ combined with a pre-processing pipeline in digital mammography has shown promising results in accurately segmenting breast masses and has the potential to significantly improve early breast cancer detection.
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2- Stephen W Duffy et al., "Mammography screening reduces rates of advanced and fatal breast cancers: Results in 549,091 women." Cancer, Vol. 126 (No. 13), pp. 2971-79, (2020).
3- James V Fiorica, "Breast cancer screening, mammography, and other modalities." Clinical obstetrics and gynecology, Vol. 59 (No. 4), pp. 688-709, (2016).
4- Kwan Hoong Ng and Malai Muttarak, "Advances in mammography have improved early detection of breast cancer." Journal-Hong Kong College of Radiologists, Vol. 6pp. 126-31, (2003).
5- Ritse M Mann et al., "Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI)." European radiology, Vol. 32 (No. 6), pp. 4036-45, (2022).
6- Leila Abdelrahman, Manal Al Ghamdi, Fernando Collado-Mesa, and Mohamed Abdel-Mottaleb, "Convolutional neural networks for breast cancer detection in mammography: A survey." Computers in biology and medicine, Vol. 131p. 104248, (2021).
7- Eman Justaniah, Areej Alhothali, and Ghadah Aldabbagh, "Mammogram segmentation techniques: A review." International Journal of Advanced Computer Science and Applications, Vol. 12 (No. 5), (2021).
8- Wajeeha Ansar and Basit Raza, "Breast cancer segmentation in mammogram using artificial intelligence and image processing: A systematic review." Current Chinese Science, Vol. 3 (No. 1), pp. 3-22, (2023).
9- Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, and Asoke K Nandi, "Medical image segmentation using deep learning: A survey." IET image processing, Vol. 16 (No. 5), pp. 1243-67, (2022).
10- Epimack Michael, He Ma, Hong Li, Frank Kulwa, and Jing Li, "Breast cancer segmentation methods: current status and future potentials." BioMed research international, Vol. 2021 (No. 1), p. 9962109, (2021).
11- Solene Bechelli, "Computer-aided cancer diagnosis via machine learning and deep learning: a comparative review." arXiv preprint arXiv:2210.11943, (2022).
12- Olaf Ronneberger, Philipp Fischer, and Thomas Brox, "U-net: Convolutional networks for biomedical image segmentation." in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, (2015): Springer, pp. 234-41.
13- Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang, "Unet++: A nested u-net architecture for medical image segmentation." in Deep learning in medical image analysis and multimodal learning for clinical decision support: 4th international workshop, DLMIA 2018, and 8th international workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, proceedings 4, (2018): Springer, pp. 3-11.
14- Hui Sun et al., "AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms." Physics in Medicine & Biology, Vol. 65 (No. 5), p. 055005, (2020).
15- N Ravitha Rajalakshmi, R Vidhyapriya, N Elango, and Nikhil Ramesh, "Deeply supervised u-net for mass segmentation in digital mammograms." International Journal of Imaging Systems and Technology, Vol. 31 (No. 1), pp. 59-71, (2021).
16- Felipe André Zeiser et al., "Segmentation of masses on mammograms using data augmentation and deep learning." Journal of digital imaging, Vol. 33pp. 858-68, (2020).
17- Asma Baccouche, Begonya Garcia-Zapirain, Cristian Castillo Olea, and Adel S Elmaghraby, "Connected-UNets: a deep learning architecture for breast mass segmentation." NPJ Breast Cancer, Vol. 7 (No. 1), p. 151, (2021).
18- Hang Min et al., "Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and mask r-cnn." in 2020 IEEE 17th International symposium on biomedical imaging (ISBI), (2020): IEEE, pp. 1111-15.
19- Inês C Moreira, Igor Amaral, Inês Domingues, António Cardoso, Maria Joao Cardoso, and Jaime S Cardoso, "Inbreast: toward a full-field digital mammographic database." Academic radiology, Vol. 19 (No. 2), pp. 236-48, (2012).
20- Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Kanae Kawai Miyake, Mia Gorovoy, and Daniel L Rubin, "A curated mammography data set for use in computer-aided detection and diagnosis research." Scientific data, Vol. 4 (No. 1), pp. 1-9, (2017).
21- D Narain Ponraj, M Evangelin Jenifer, P Poongodi, and J Samuel Manoharan, "A survey on the preprocessing techniques of mammogram for the detection of breast cancer." Journal of Emerging Trends in Computing and Information Sciences, Vol. 2 (No. 12), pp. 656-64, (2011).
22- Ardra Mariya Joseph, M Grace John, and Anto Sahaya Dhas, "Mammogram image denoising filters: A comparative study." in 2017 Conference on emerging devices and smart systems (ICEDSS), (2017): IEEE, pp. 184-89.
23- Indra Kanta Maitra, Sanjay Nag, and Samir Kumar Bandyopadhyay, "Technique for preprocessing of digital mammogram." Computer methods and programs in biomedicine, Vol. 107 (No. 2), pp. 175-88, (2012).
24- R Ramani, N Suthanthira Vanitha, and S Valarmathy, "The pre-processing techniques for breast cancer detection in mammography images." International Journal of Image, Graphics and Signal Processing, Vol. 5 (No. 5), p. 47, (2013).
25- Antoni Buades, Bartomeu Coll, and J-M Morel, "A non-local algorithm for image denoising." in 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), (2005), Vol. 2: Ieee, pp. 60-65.
26- G Arvind Kumar and Ashok Kusagur, "Evaluation of image denoising techniques a performance perspective." in 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), (2016): IEEE, pp. 1836-39.
27- Etta D Pisano et al., "Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms." Journal of digital imaging, Vol. 11pp. 193-200, (1998).
28- Connor Shorten and Taghi M Khoshgoftaar, "A survey on image data augmentation for deep learning." Journal of big data, Vol. 6 (No. 1), pp. 1-48, (2019).
29- Alberto Garcia-Garcia, S Orts-Escolano, S Oprea, V Villena-Martinez, and J Garcia-Rodriguez, "A review on deep learning techniques applied to semantic segmentation. arXiv 2017." arXiv preprint arXiv:1704.06857, (2020).
30- Imran Qureshi et al., "Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends." Information Fusion, Vol. 90pp. 316-52, (2023).
31- Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia, "Pyramid scene parsing network." in Proceedings of the IEEE conference on computer vision and pattern recognition, (2017), pp. 2881-90.
32- Abhishek Chaurasia and Eugenio Culurciello, "Linknet: Exploiting encoder representations for efficient semantic segmentation." in 2017 IEEE visual communications and image processing (VCIP), (2017): IEEE, pp. 1-4.
33- Dominik Müller, Iñaki Soto-Rey, and Frank Kramer, "Towards a guideline for evaluation metrics in medical image segmentation." BMC Research Notes, Vol. 15 (No. 1), p. 210, (2022).
34- Carole H Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, and M Jorge Cardoso, "Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations." in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 3, (2017): Springer, pp. 240-48.
35- Antonio Galli, Stefano Marrone, Gabriele Piantadosi, Mario Sansone, and Carlo Sansone, "A pipelined tracer-aware approach for lesion segmentation in breast DCE-MRI." Journal of imaging, Vol. 7 (No. 12), p. 276, (2021).
36- Helene Berment, V Becette, M Mohallem, F Ferreira, and P Chérel, "Masses in mammography: What are the underlying anatomopathological lesions?" Diagnostic and interventional imaging, Vol. 95 (No. 2), pp. 124-33, (2014).
37- Song Liu, Xiao-Dong Wu, Wen-Jian Xu, Qing Lin, Xue-Jun Liu, and Ying Li, "Is there a correlation between the presence of a spiculated mass on mammogram and luminal a subtype breast cancer?" Korean journal of radiology, Vol. 17 (No. 6), pp. 846-52, (2016).
38- Lazaros Tsochatzidis, Panagiota Koutla, Lena Costaridou, and Ioannis Pratikakis, "Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses." Computer methods and programs in biomedicine, Vol. 200p. 105913, (2021).
39- Yongye Su, Qian Liu, Wentao Xie, and Pingzhao Hu, "YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms." Computer methods and programs in biomedicine, Vol. 221p. 106903, (2022).
40- Ghada M El-Banby, Nourhan S Salem, Eman A Tafweek, and Essam N Abd El-Azziz, "Automated abnormalities detection in mammography using deep learning." Complex & Intelligent Systems, Vol. 10 (No. 5), pp. 7279-95, (2024).
2- Stephen W Duffy et al., "Mammography screening reduces rates of advanced and fatal breast cancers: Results in 549,091 women." Cancer, Vol. 126 (No. 13), pp. 2971-79, (2020).
3- James V Fiorica, "Breast cancer screening, mammography, and other modalities." Clinical obstetrics and gynecology, Vol. 59 (No. 4), pp. 688-709, (2016).
4- Kwan Hoong Ng and Malai Muttarak, "Advances in mammography have improved early detection of breast cancer." Journal-Hong Kong College of Radiologists, Vol. 6pp. 126-31, (2003).
5- Ritse M Mann et al., "Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI)." European radiology, Vol. 32 (No. 6), pp. 4036-45, (2022).
6- Leila Abdelrahman, Manal Al Ghamdi, Fernando Collado-Mesa, and Mohamed Abdel-Mottaleb, "Convolutional neural networks for breast cancer detection in mammography: A survey." Computers in biology and medicine, Vol. 131p. 104248, (2021).
7- Eman Justaniah, Areej Alhothali, and Ghadah Aldabbagh, "Mammogram segmentation techniques: A review." International Journal of Advanced Computer Science and Applications, Vol. 12 (No. 5), (2021).
8- Wajeeha Ansar and Basit Raza, "Breast cancer segmentation in mammogram using artificial intelligence and image processing: A systematic review." Current Chinese Science, Vol. 3 (No. 1), pp. 3-22, (2023).
9- Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, and Asoke K Nandi, "Medical image segmentation using deep learning: A survey." IET image processing, Vol. 16 (No. 5), pp. 1243-67, (2022).
10- Epimack Michael, He Ma, Hong Li, Frank Kulwa, and Jing Li, "Breast cancer segmentation methods: current status and future potentials." BioMed research international, Vol. 2021 (No. 1), p. 9962109, (2021).
11- Solene Bechelli, "Computer-aided cancer diagnosis via machine learning and deep learning: a comparative review." arXiv preprint arXiv:2210.11943, (2022).
12- Olaf Ronneberger, Philipp Fischer, and Thomas Brox, "U-net: Convolutional networks for biomedical image segmentation." in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, (2015): Springer, pp. 234-41.
13- Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang, "Unet++: A nested u-net architecture for medical image segmentation." in Deep learning in medical image analysis and multimodal learning for clinical decision support: 4th international workshop, DLMIA 2018, and 8th international workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, proceedings 4, (2018): Springer, pp. 3-11.
14- Hui Sun et al., "AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms." Physics in Medicine & Biology, Vol. 65 (No. 5), p. 055005, (2020).
15- N Ravitha Rajalakshmi, R Vidhyapriya, N Elango, and Nikhil Ramesh, "Deeply supervised u-net for mass segmentation in digital mammograms." International Journal of Imaging Systems and Technology, Vol. 31 (No. 1), pp. 59-71, (2021).
16- Felipe André Zeiser et al., "Segmentation of masses on mammograms using data augmentation and deep learning." Journal of digital imaging, Vol. 33pp. 858-68, (2020).
17- Asma Baccouche, Begonya Garcia-Zapirain, Cristian Castillo Olea, and Adel S Elmaghraby, "Connected-UNets: a deep learning architecture for breast mass segmentation." NPJ Breast Cancer, Vol. 7 (No. 1), p. 151, (2021).
18- Hang Min et al., "Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and mask r-cnn." in 2020 IEEE 17th International symposium on biomedical imaging (ISBI), (2020): IEEE, pp. 1111-15.
19- Inês C Moreira, Igor Amaral, Inês Domingues, António Cardoso, Maria Joao Cardoso, and Jaime S Cardoso, "Inbreast: toward a full-field digital mammographic database." Academic radiology, Vol. 19 (No. 2), pp. 236-48, (2012).
20- Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Kanae Kawai Miyake, Mia Gorovoy, and Daniel L Rubin, "A curated mammography data set for use in computer-aided detection and diagnosis research." Scientific data, Vol. 4 (No. 1), pp. 1-9, (2017).
21- D Narain Ponraj, M Evangelin Jenifer, P Poongodi, and J Samuel Manoharan, "A survey on the preprocessing techniques of mammogram for the detection of breast cancer." Journal of Emerging Trends in Computing and Information Sciences, Vol. 2 (No. 12), pp. 656-64, (2011).
22- Ardra Mariya Joseph, M Grace John, and Anto Sahaya Dhas, "Mammogram image denoising filters: A comparative study." in 2017 Conference on emerging devices and smart systems (ICEDSS), (2017): IEEE, pp. 184-89.
23- Indra Kanta Maitra, Sanjay Nag, and Samir Kumar Bandyopadhyay, "Technique for preprocessing of digital mammogram." Computer methods and programs in biomedicine, Vol. 107 (No. 2), pp. 175-88, (2012).
24- R Ramani, N Suthanthira Vanitha, and S Valarmathy, "The pre-processing techniques for breast cancer detection in mammography images." International Journal of Image, Graphics and Signal Processing, Vol. 5 (No. 5), p. 47, (2013).
25- Antoni Buades, Bartomeu Coll, and J-M Morel, "A non-local algorithm for image denoising." in 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), (2005), Vol. 2: Ieee, pp. 60-65.
26- G Arvind Kumar and Ashok Kusagur, "Evaluation of image denoising techniques a performance perspective." in 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), (2016): IEEE, pp. 1836-39.
27- Etta D Pisano et al., "Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms." Journal of digital imaging, Vol. 11pp. 193-200, (1998).
28- Connor Shorten and Taghi M Khoshgoftaar, "A survey on image data augmentation for deep learning." Journal of big data, Vol. 6 (No. 1), pp. 1-48, (2019).
29- Alberto Garcia-Garcia, S Orts-Escolano, S Oprea, V Villena-Martinez, and J Garcia-Rodriguez, "A review on deep learning techniques applied to semantic segmentation. arXiv 2017." arXiv preprint arXiv:1704.06857, (2020).
30- Imran Qureshi et al., "Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends." Information Fusion, Vol. 90pp. 316-52, (2023).
31- Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia, "Pyramid scene parsing network." in Proceedings of the IEEE conference on computer vision and pattern recognition, (2017), pp. 2881-90.
32- Abhishek Chaurasia and Eugenio Culurciello, "Linknet: Exploiting encoder representations for efficient semantic segmentation." in 2017 IEEE visual communications and image processing (VCIP), (2017): IEEE, pp. 1-4.
33- Dominik Müller, Iñaki Soto-Rey, and Frank Kramer, "Towards a guideline for evaluation metrics in medical image segmentation." BMC Research Notes, Vol. 15 (No. 1), p. 210, (2022).
34- Carole H Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, and M Jorge Cardoso, "Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations." in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 3, (2017): Springer, pp. 240-48.
35- Antonio Galli, Stefano Marrone, Gabriele Piantadosi, Mario Sansone, and Carlo Sansone, "A pipelined tracer-aware approach for lesion segmentation in breast DCE-MRI." Journal of imaging, Vol. 7 (No. 12), p. 276, (2021).
36- Helene Berment, V Becette, M Mohallem, F Ferreira, and P Chérel, "Masses in mammography: What are the underlying anatomopathological lesions?" Diagnostic and interventional imaging, Vol. 95 (No. 2), pp. 124-33, (2014).
37- Song Liu, Xiao-Dong Wu, Wen-Jian Xu, Qing Lin, Xue-Jun Liu, and Ying Li, "Is there a correlation between the presence of a spiculated mass on mammogram and luminal a subtype breast cancer?" Korean journal of radiology, Vol. 17 (No. 6), pp. 846-52, (2016).
38- Lazaros Tsochatzidis, Panagiota Koutla, Lena Costaridou, and Ioannis Pratikakis, "Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses." Computer methods and programs in biomedicine, Vol. 200p. 105913, (2021).
39- Yongye Su, Qian Liu, Wentao Xie, and Pingzhao Hu, "YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms." Computer methods and programs in biomedicine, Vol. 221p. 106903, (2022).
40- Ghada M El-Banby, Nourhan S Salem, Eman A Tafweek, and Essam N Abd El-Azziz, "Automated abnormalities detection in mammography using deep learning." Complex & Intelligent Systems, Vol. 10 (No. 5), pp. 7279-95, (2024).
| Files | ||
| Issue | Vol 12 No 4 (2025) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v12i4.19818 | |
| Keywords | ||
| Breast Cancer Semantic Segmentation UNet Digital Mammography Deep Learning | ||
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How to Cite
1.
Jalalian K, Hosseini G, Ghiasi R, Bosaghzadeh A. Enhancing Breast Cancer Detection in Mammography with UNet++ - Deep Learning Approach. Frontiers Biomed Technol. 2025;12(4):812-829.
