Abstract
Breast cancer is commonly recognized as the second most frequent malignancy in women worldwide. Breast cancer therapy includes surgical surgery, radiation therapy, and medication which can be exceedingly successful, with 90% or higher survival rates, especially when the condition is discovered early. This work is one such approach for early detection of breast cancer relying on the BI-RADS score. In this regard, a computer-aided-diagnosis system based on a bespoke Digital Mammogram Diagnostic Convolutional Neural Network (DMD-CNN) model that can aid in the categorization of mammogram breast lesions is proposed. Furthermore, a PYNQ-based acceleration through the Artix 7 FPGA is employed for deployment of DMD-CNN model’s hardware acceleration platform which is the first of its kind for breast cancer, yielding a performance accuracy of 98.2%, the proposed model exceeded the state-of-the-art approach. The comparative analysis performed in the study has shown that the proposed method has resulted in a 4% increase in accuracy and a good recognition rate of 96% when compared to the existing model. A k-fold cross-validation (k = 5, 7, 9 the reported accuracy score values are 96.2%, 97.5% and 98.1%, respectively) approach was used to test and assess the integrated system. Extensive testing using mammography datasets was carried out to determine the increased performance of the suggested approach. Experiments reveal that when compared to the DMD-CNN model acceleration to GPU, the suggested solution not only optimizes resource utilization but also decreases power consumption to 3.12 W. Hardware acceleration through FPGA resulted in processing and analyzing nearly 91 images in a second where a single image will be processed using CPU.
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Data availability
The other data that support the findings of this study were collected from SRM Medical Hospital and Research Centre but restrictions apply to the availability of these data, which were used under ethical clearance for the current study, and so are not publicly available. Data can be obtained from the authors upon reasonable request and with permission of SRM Medical Hospital and Research Centre.
References
https://www.who.int/news-room/fact-sheets/detail/breast-cancer
Hassan, S.A., Sayed, M.S., Abdalla, M.I., Rashwan, M.A.: Breast cancer masses classification using deep convolutional neural networks and transfer learning. Multimed. Tools Appl. 79, 30735–30768 (2020). https://doi.org/10.1007/s11042-020-09518-w
Weerakkoddy, Y., Yap, J.: Breast imaging reporting and data system (BI-RADS) https://doi.org/10.53347/rlD-10003
Pu, J., Bell, S., Yang, X., Setter, J., Richardson, S., Ragan-Kelley, J., Horowitz, M.: Programming heterogeneous systems from an image processing DSL. ACM Trans. Archit. Code Optim. 14, 1–25 (2017). https://doi.org/10.1145/3107953
HajiRassouliha, A., Taberner, A.J., Nash, M.P., Nielsen, P.M.F.: Suitability of recent hardware accelerators (DSPs, FPGAs, and GPUs) for computer vision and image processing algorithms. Signal Process. Image Commun. 68, 101–119 (2018). https://doi.org/10.1016/j.image.2018.07.007
Agarwal, R., Diaz, O., Lladó, X., Yap, M.H., Martí, R.: Automatic mass detection in mammograms using deep convolutional neural networks. J. Med. Imaging. 6, 1 (2019). https://doi.org/10.1117/1.jmi.6.3.031409
Al-antari, M.A., Al-masni, M.A., Choi, M.T., Han, S.M., Kim, T.S.: A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int. J. Med. Inform. 117, 44–54 (2018). https://doi.org/10.1016/j.ijmedinf.2018.06.003
Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8, 1–7 (2018). https://doi.org/10.1038/s41598-018-22437-z
Ting, F.F., Tan, Y.J., Sim, K.S.: Convolutional neural network improvement for breast cancer classification. Expert Syst. Appl. 120, 103–115 (2019). https://doi.org/10.1016/j.eswa.2018.11.008
Saber, A., Sakr, M., Abo-Seida, O.M., Keshk, A., Chen, H.: A Novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access. 9, 71194–71209 (2021). https://doi.org/10.1109/ACCESS.2021.3079204
Oyelade, O.N., Ezugwu, A.E.S.: A state-of-the-art survey on deep learning methods for detection of architectural distortion from digital mammography. IEEE Access. 8, 148644–148676 (2020). https://doi.org/10.1109/ACCESS.2020.3016223
Sanaullah, A., Yang, C., Alexeev, Y., Yoshii, K., Herbordt, M.C.: Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks. BMC Bioinformatics 19, 19–31 (2018). https://doi.org/10.1186/s12859-018-2505-7
Xiong, S., Wu, G., Fan, X., Feng, X., Huang, Z., Cao, W., Zhou, X., Ding, S., Yu, J., Wang, L., Shi, Z.: MRI-based brain tumor segmentation using FPGA-accelerated neural network. BMC Bioinformatics 22, 1–15 (2021). https://doi.org/10.1186/s12859-021-04347-6
Jin, J., Fu, K., Zhang, C.: Traffic sign recognition with hinge loss trained convolutional neural networks. IEEE Trans. Intell. Transp. Syst. 15, 1991–2000 (2014). https://doi.org/10.1109/TITS.2014.2308281
Deepak, S., Ameer, P.M.: Automated categorization of brain tumor from MRI using CNN features and SVM. J. Ambient Intell. Humaniz. Comput. 12, 8357–8369 (2021). https://doi.org/10.1007/s12652-020-02568-w
Hanh, B.T., Van Manh, H., Nguyen, N.-V.: Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification. J. Plant Dis. Prot. (2022). https://doi.org/10.1007/s41348-022-00601-y
Panchbhaiyye, V., Ogunfunmi, T.: An efficient FIFO based accelerator for convolutional neural networks. J. Signal Process. Syst. 93, 1117–1129 (2021). https://doi.org/10.1007/s11265-020-01632-0
Overlay Tutorial—Python Productivity for Zynq (Pynq) v1.0. 2020 (2020)
Xilinx, I.: Vivado Design, http://www.xilinx.com/support/documentation/ip_documentation/axi_ref_guide/latest/ug1037-vivado-axi-reference-guide.pdf, (2015)
Malarvizhi, S., Kayalvizhi, R., Kumar, A., Topkar, A.: Raw data processing using modern hardware for inspection of objects in X-Ray baggage inspection systems. IEEE Trans. Nucl. Sci. 68, 1296–1303 (2021). https://doi.org/10.1109/TNS.2021.3075256
Liu, H., Chen, Y., Zhang, Y., Wang, L., Luo, R., Wu, H., Wu, C., Zhang, H., Tan, W., Yin, H., Wang, D.: A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening Artificial intelligence AUC Area under the receiver operating characteristic curve BI-RADS. 5902–5912 (2021)
Tsai, K.J., Chou, M.C., Li, H.M., Liu, S.T., Hsu, J.H., Yeh, W.C., Hung, C.M., Yeh, C.Y., Hwang, S.H.: A high-performance deep neural network model for BI-RADS classification of screening mammography. Sensors. 22, (2022). https://doi.org/10.3390/s22031160
Maqsood, S., Damaševičius, R., Maskeliūnas, R.: TTCNN: A breast cancer detection and classification towards computer-aided diagnosis using digital mammography in early stages. Appl. Sci. (2022). https://doi.org/10.3390/app12073273
Meraj, T., Alosaimi, W., Alouffi, B., Rauf, H.T., Kumar, S.A., Damaševičius, R., Alyami, H.: A quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data. PeerJ Comput Sci (2021). https://doi.org/10.7717/PEERJ-CS.805
Acknowledgements
The authors would like to acknowledge Xilinx, Inc. for donating the hardware boards and systems utilized in this study. The authors would also like to acknowledge the radiologist Dr A.Senthil Kumar and his team for their valuable input and guidance throughout the study.
Funding
This work was funded by Xilinx Women In Technology (WIT) Grant Spring 2021 to SRM Institute of Science and Technology, Kattankulathur, Chennai.
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The authors Ms.H. Heartlin Maria and R.Kayalvizhi carried out the software and hardware experimental analysis, wrote the manuscript and prepared the figures.The authors Dr.S.Malarvizhi and Dr.Revathi formulated the problem statement and supervised the simulation and hardware implementation, revised and proof-read the manuscript. Dr.Shantanu Patil and Dr. Senthil verified the real-time performance of the proposed model on par with the ground truth.
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Maria, H.H., Kayalvizhi, R., Malarvizhi, S. et al. Real-time deployment of BI-RADS breast cancer classifier using deep-learning and FPGA techniques. J Real-Time Image Proc 20, 80 (2023). https://doi.org/10.1007/s11554-023-01335-2
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DOI: https://doi.org/10.1007/s11554-023-01335-2