An efficient hardware implementation of Convolutional Neural Network in detect Breast Cancer Histopathology Image

Phat Tan Vo, Hoang Trang


This paper presents our work on evaluating the effectiveness of a novel deep convolutional neural network architecture (CNN) for classifying breast histology images for cancer risk factors as negative or positive. Also, the hardware structure of the proposed model was successfully synthesized and verified. The results indicate that a CNN trained on a small dataset achieved an overall AUC (Area under ROC Curve - ROC is an acronym for receiver operating characteristic) value of 0.922 across a set of 55505 test images. In addition, the time it takes to classify each image is within 3.8 milliseconds instead of a task that even trained pathologists take hours to complete.

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Swathi, T. V., Krishna, S., & Ramesh, M. V. (2019, March). A Survey on Breast Cancer Diagnosis Methods and Modalities. In 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET) (pp. 287-292). IEEE.

DeSantis, C. E., Bray, F., Ferlay, J., Lortet-Tieulent, J., Anderson, B. O., & Jemal, A. (2015). International variation in female breast cancer incidence and mortality rates. Cancer Epidemiology and Prevention Biomarkers, 24(10), 1495-1506.

Ginsburg, O., Yip, C. H., Brooks, A., Cabanes, A., Caleffi, M., Dunstan Yataco, J. A., ... & Anderson, B. O. (2020). Breast cancer early detection: A phased approach to implementation. Cancer, 126, 2379-2393.

Rositch, A. F., Unger‐Saldaña, K., DeBoer, R. J., Ng’ang’a, A., & Weiner, B. J. (2020). The role of dissemination and implementation science in global breast cancer control programs: frameworks, methods, and examples. Cancer, 126, 2394-2404.

Kajala, A., & Jain, V. K. (2020, February). Diagnosis of Breast Cancer using Machine Learning Algorithms-A Review. In 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3) (pp. 1-5). IEEE.

Laghmati, S., Tmiri, A., & Cherradi, B. (2019). Machine Learning based System for Prediction of Breast Cancer Severity. In 2019 International Conference on Wireless Networks and Mobile Communications (WINCOM) (pp. 1-5). IEEE.

Gupta, A., Kaushik, D., Garg, M., & Verma, A. (2020, October). Machine Learning model for Breast Cancer Prediction. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 472-477). IEEE.

El Rahman, S. A., Al-montasheri, A., Al-hazmi, B., Al-dkaan, H., & Al-shehri, M. (2019, February). Machine Learning Model for Breast Cancer Prediction. In 2019 International Conference on Fourth Industrial Revolution (ICFIR) (pp. 1-8). IEEE.

Singh, O. V., & Choudhary, P. (2019, February). A Study on Convolution Neural Network for Breast Cancer Detection. In 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP) (pp. 1-7). IEEE.

Omonigho, E. L., David, M., Adejo, A., & Aliyu, S. (2020, March). Breast Cancer: Tumor Detection in Mammogram Images

Using Modified AlexNet Deep Convolution Neural Network. In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS) (pp. 1-6). IEEE.

Yamlome, P., Akwaboah, A. D., Marz, A., & Deo, M. (2020, July). Convolutional Neural Network Based Breast Cancer Histopathology Image Classification. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1144-1147). IEEE.

Roy, A. (2019, October). Deep convolutional neural networks for breast cancer detection. In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0169-0171). IEEE.

Narayanan, B. N., Krishnaraja, V., & Ali, R. (2019, July). Convolutional Neural Network for Classification of Histopathology Images for Breast Cancer Detection. In 2019 IEEE National Aerospace and Electronics Conference (NAECON) (pp. 291-295). IEEE.

Shahidi, F., Daud, S. M., Abas, H., Ahmad, N. A., & Maarop, N. (2020). Breast Cancer Classification Using Deep Learning Approaches and Histopathology Image: A Comparison Study. IEEE Access, 8, 187531-187552.

Kieffer, B., Babaie, M., Kalra, S., & Tizhoosh, H. R. (2017, November). Convolutional neural networks for histopathology image classification: Training vs. using pre-trained networks. In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1-6). IEEE.

Adeshina, S. A., Adedigba, A. P., Adeniyi, A. A., & Aibinu, A. M. (2018). Breast cancer histopathology image classification with deep convolutional neural networks. In 2018 14th international conference on electronics computer and computation (ICECCO) (pp. 206-212). IEEE.

Patil, A., Tamboli, D., Meena, S., Anand, D., & Sethi, A. (2019, November). Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning. In 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) (pp. 1-4). IEEE.

Angara, S., Robinson, M., & Guillén-Rondon, P. (2018, December). Convolutional neural networks for breast cancer histopathological image classification. In 2018 4th International Conference on Big Data and Information Analytics (BigDIA) (pp. 1-6). IEEE.

Xiang, Z., Ting, Z., Weiyan, F., & Cong, L. (2019, June). Breast cancer diagnosis from histopathological image based on deep learning. In 2019 Chinese Control And Decision Conference (CCDC) (pp. 4616-4619). IEEE.

Ding, R., Tian, X., Bai, G., Su, G., & Wu, X. (2019). Hardware Implementation of Convolutional Neural Network for Face Feature Extraction. In 2019 IEEE 13th International Conference on ASIC (ASICON) (pp. 1-4). IEEE.

Farrukh, F. U. D., Xie, T., Zhang, C., & Wang, Z. (2018, November). Optimization for efficient hardware implementation of CNN on FPGA. In 2018 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA) (pp. 88-89). IEEE.

Chen, W., Wang, Y., Yang, C., & Li, Y. (2020, June). Hardware Acceleration Implementation of Three-Dimensional Convolutional Neural Network on Vector Digital Signal Processors. In 2020 4th International Conference on Robotics and Automation Sciences (ICRAS) (pp. 122-129). IEEE.

Janowczyk, A., & Madabhushi, A. (2016). “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”. Journal of pathology informatics, 7.

Cruz-Roa, A., Basavanhally, A., González, F., Gilmore, H., Feldman, M., Ganesan, S., ... & Madabhushi, A. (2014, March). “Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks”. In Medical Imaging 2014: Digital Pathology (Vol. 9041, p. 904103). International Society for Optics and Photonics.

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Kudlur, M. (2016). Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (pp. 265-283).

Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N. N., ... & Madabhushi, A. (2017). Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent. Scientific reports, 7(1), 1-14.

Khan, S., Rahmani, H., Shah, S. A. A., & Bennamoun, M. (2018). A guide to convolutional neural networks for computer vision. Synthesis Lectures on Computer Vision, 8(1), 1-207.

Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media.

Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT press.

Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In 2015 Third International Conference for Learning Representations (ICLR ), 13.

Zeiler, M. D. (2012). Adadelta: An adaptive learning rate method. arXiv:1212.5701.

Noronha, D. H., Salehpour, B., & Wilton, S. J. (2018, August). LeFlow: Enabling flexible FPGA high-level synthesis of tensorflow deep neural networks. In FSP Workshop 2018; Fifth International Workshop on FPGAs for Software Programmers (pp. 1-8). VDE.

Lattner, C., & Adve, V. (2004, March). LLVM: A compilation framework for lifelong program analysis & transformation. In International Symposium on Code Generation and Optimization, 2004. CGO 2004. (pp. 75-86). IEEE.

Canis, A., Choi, J., Aldham, M., Zhang, V., Kammoona, A., Czajkowski, T., ... & Anderson, J. H. (2013). LegUp: An open-source high-level synthesis tool for FPGA-based processor/accelerator systems. ACM Transactions on Embedded Computing Systems (TECS), 13(2), 1-27.


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