Breast Tumour Segmentation Using Advanced UNet with Saliency, Channel, and Spatial Attention Models

Main Article Content

Vaishali Dasharath Shinde, Jyoti Surve, Shrikant J. Honade, Ketaki Naik,Swarna Kuchibhotla, Sunil L. Bangare, Dhanraj Jadhav


Cancers are getting pretty common these days and in that the second most common cancer in the world after lung cancer is breast cancer. The primary screening techniques for early diagnosis of cancerous nodules in women breast are Ultrasound, Mammography, and MRI analysis. However, accurately identifying and outlining the boundaries of tumor regions remains challenging due to their unpredictable shapes, random variations, and blurry outlines. Currently, the CAD based image analysis systems have gained huge attention in biomedical image processing systems along with Machine Learning (ML) and Deep Learning (DL) methods. The conventional machine learning methods suffer from accuracy related issues therefore deep learning-based schemes have been adopted. Specifically, UNet based approach is widely employed for biomedical image segmentation. This research focuses on development of a UNet based deep learning architecture for breast cancer image analysis and segmentation. The proposed model improves the segmentation accuracy by adding saliency model, channel and spatial attention model to consider the low- and high-level features. These modules help to achieve the clear boundary during segmentation. The performance of proposed model is evaluated by using publicly available mammogram dataset known as DDSM dataset.

Article Details

Author Biography

Vaishali Dasharath Shinde, Jyoti Surve, Shrikant J. Honade, Ketaki Naik,Swarna Kuchibhotla, Sunil L. Bangare, Dhanraj Jadhav

[1]Dr. Vaishali Dasharath Shinde

2Dr. Jyoti Surve

3Dr. Shrikant J. Honade

5Dr. Ketaki Naik

6Dr. Swarna Kuchibhotla

7Dr. Sunil L. Bangare

8Dr. Dhanraj Jadhav


[1] CSE Department, K. L. E. F. (K. L. University), A.P., India., ORCID: 0000-0002-0522-5239

2Associate Professor, Department of Information Technology, International Institute of Information Technology, Hinjewadi, Savitribai Phule Pune University, Pune, India, ORCID:0000-0002-7898-9109

4Associate Professor & HoD, Department of Electronics Engineering (VLSI D & T), CSMSS Chh. Shahu College of Engineering, Kanchawadi Chh. Sambhajinagar, Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, India,, ORCID: 0000-0003-3601-2073

5Associate Professor, Department of Information Technology, Bharati Vidyapeeth's College of Engineering for Women, Savitribai Phule Pune University, Pune, India, ORCID: 0000-0002-3941-8370

6Associate Professor, CSE Department, K. L. E. F. (K. L. University), A.P., India ORCID: 0000-0001-9407-9714

7Associate Professor, Department of Information Technology, Sinhgad Academy of Engineering, Savitribai Phule Pune University, Pune, India,, ORCID: 0000-0002-7669-6361

 8Assistant Professor, Department of Computer Engineering, JSPM's Rajarshi Shahu College of Engineering Tathawade, Pune. Orchid id: 0000-0003-4376-8223

Corresponding authors:,



Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), 394-424.

Subashini T, Ramalingam V, Palanivel S. Automated assessment of breast tissue density in digital mammograms. Comput Vision Image Understanding 2010;114: 33–43. How Common Is Breast Cancer?, 2020.

Ferlay, J., Ervik, M., Lam, F., Colombet, M., Mery, L., Piñeros, M., ... & Bray, F. (2018). Global cancer observatory: cancer today. Lyon, France: international agency for research on cancer, 3(20), 2019.

Ruvio, G., Solimene, R., Cuccaro, A., Fiaschetti, G., Fagan, A. J., Cournane, S., ... & Browne, J. E. (2020). Multimodal breast phantoms for microwave, ultrasound, mammography, magnetic resonance and computed tomography imaging. Sensors, 20(8), 2400.

Li, H., Zhang, S., Wang, Q., & Zhu, R. (2016). Clinical value of mammography in diagnosis and identification of breast mass. Pakistan journal of medical sciences, 32(4), 1020.

Xu, M., Huang, K., & Qi, X. (2023). A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification. IEEE Access.

Osman, F. M., & Yap, M. H. (2018). The effect of filtering algorithms for breast ultrasound lesions segmentation. Informatics in Medicine Unlocked, 12, 14-20.

Daoud, M. I., Atallah, A. A., Awwad, F., Al-Najjar, M., & Alazrai, R. (2019). Automatic superpixel-based segmentation method for breast ultrasound images. Expert Systems with Applications, 121, 78-96.

Krawczyk, B., Galar, M., Jeleń, Ł., & Herrera, F. (2016). Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy. Applied Soft Computing, 38, 714-726.

Aswathy, M. A., & Jagannath, M. (2021). An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features. Medical & biological engineering & computing, 59(9), 1773-1783.

Farhan, A. H., & Kamil, M. Y. (2020, May). Texture analysis of mammogram using local binary pattern method. In Journal of Physics: Conference Series (Vol. 1530, No. 1, p. 012091). IOP Publishing.

Pratiwi, M., Harefa, J., & Nanda, S. (2015). Mammograms classification using gray-level co-occurrence matrix and radial basis function neural network. Procedia Computer Science, 59, 83-91.

Farhan, A. H., & Kamil, M. Y. (2020, November). Texture Analysis of Breast Cancer via LBP, HOG, and GLCM techniques. In IOP conference series: materials science and engineering (Vol. 928, No. 7, p. 072098). IOP Publishing.

Matos, C. E. F., Souza, J. C., Diniz, J. O. B., Junior, G. B., de Paiva, A. C., de Almeida, J. D. S., ... & Silva, A. C. (2019). Diagnosis of breast tissue in mammography images based local feature descriptors. Multimedia Tools and Applications, 78, 12961-12986.

Ahmed, L., Iqbal, M. M., Aldabbas, H., Khalid, S., Saleem, Y., & Saeed, S. (2020). Images data practices for semantic segmentation of breast cancer using deep neural network. Journal of Ambient Intelligence and Humanized Computing, 1-17.

Dhungel, N., Carneiro, G., & Bradley, A. P. (2017). A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Medical image analysis, 37, 114-128.

Geras, K. J., Wolfson, S., Shen, Y., Wu, N., Kim, S., Kim, E., ... & Cho, K. (2017). High-resolution breast cancer screening with multi-view deep convolutional neural networks. arXiv preprint arXiv:1703.07047.

Shen, R., Yao, J., Yan, K., Tian, K., Jiang, C., & Zhou, K. (2020). Unsupervised domain adaptation with adversarial learning for mass detection in mammogram. Neurocomputing, 393, 27-37.

Iqbal, A., & Sharif, M. (2023). BTS-ST: Swin transformer network for segmentation and classification of multimodality breast cancer images. Knowledge-Based Systems, 267, 110393.

Inan, M. S. K., Alam, F. I., & Hasan, R. (2022). Deep integrated pipeline of segmentation guided classification of breast cancer from ultrasound images. Biomedical Signal Processing and Control, 75, 103553.

Umer, M. J., Sharif, M., & Wang, S. H. (2022). Breast cancer classification and segmentation framework using multiscale CNN and U‐shaped dual decoded attention network. Expert Systems, e13192.

Jabeen, K., Khan, M. A., Alhaisoni, M., Tariq, U., Zhang, Y. D., Hamza, A., ... & Damaševičius, R. (2022). Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion. Sensors, 22(3), 807.

Maqsood, S., Damaševičius, R., & Maskeliūnas, R. (2022). TTCNN: A breast cancer detection and classification towards computer-aided diagnosis using digital mammography in early stages. Applied Sciences, 12(7), 3273.

Ronneberger, O., Fischer, P., & Brox, T. (2015). 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 (pp. 234-241). Springer International Publishing.

Ren S, He K, Girshick RB, Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In: Conference on Neural Information Processing Systems; 2015. p. 91-99

Redmon J, Divvala SK, Girshick RB, Farhadi A. You Only Look Once: Unified, Real-Time Object Detection. In: IEEE Conference on Computer Vision and Pattern Recognition; 2016. p. 779-788.

Lin T, Dolla´r P, Girshick RB, He K, Hariharan B, Belongie SJ. Feature Pyramid Networks for Object Detection. In: IEEE Conference on Computer Vision and Pattern Recognition; 2017. p. 936-944.

He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV); 2017. p. 2980-2988

Liu W, Anguelov D, Erhan D, Szegedy C, Reed SE, Fu C, et al. SSD: Single Shot MultiBox Detector. In: European Conference on Computer Vision; 2016. p. 21-37.

Cai Z, Vasconcelos N. Cascade R-CNN: Delving Into High Quality Object Detection. In: IEEE Conference on Computer Vision and Pattern Recognition; 2018. p. 6154-6162.

He, J., Wang, J., Han, Z., Li, B., Lv, M., & Shi, Y. (2023). Cancer detection for small-size and ambiguous tumors based on semantic FPN and transformer. PloS one, 18(2), e0275194.

Chougrad H, Zouaki H, Alheyane O (2018) Deep convolutional neural networks for breast cancer screening. Comput Methods Prog Biomed 157:19–30

Jiao Z, Gao X, Wang Y, Li J (2016) A deep feature based framework for breast masses classification. Neurocomputing 197:221–231

Al-Masni MA, Al-Antari MA, Park J-M, Gi G, Kim T-Y, Rivera P, Valarezo E, Choi M-T, Han S-M, Kim T-S (2018) Simultaneous detection and classification of breast masses in digital mammograms via a deep learning yolo-based cad system. Comput Methods Prog Biomed 157:85–94

Chen Y, Zhang Q, Wu Y, Liu B, Wang M, Lin Y (2018) Finetuning resnet for breast cancer classification from mammography. In: The international conference on healthcare science and engineering. Springer, pp 83–96

Houssein, E. H., Emam, M. M., & Ali, A. A. (2022). An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm. Neural Computing and Applications, 34(20), 18015-18033.

Ragab DA, Attallah O, Sharkas M, Ren J, Marshall S (2021) A framework for breast cancer classification using multi-dcnns. Comput Biol Med 131:104245

Ragab Dina A, Sharkas Maha, Marshall Stephen, Ren Jinchang (2019) Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ, 7:e6201

Khan HN, Shahid AR, Raza B, Dar AH, Alquhayz H (2019) Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access 7:165724–165733

Song R, Li T, Wang Y (2020) Mammographic classification based on xgboost and dcnn with multi features. IEEE Access 8:75011–75021

Zhang H, Renzhong W, Yuan T, Jiang Z, Huang S, Jinpeng W, Hua J, Niu Z, Ji D (2020) De-ada*: a novel model for breast mass classification using cross-modal pathological semantic mining and organic integration of multi-feature fusions. Inf Sci 539:461–486