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Design of integrated interactive system for pre-diagnosis of breast cancer pathological images based on CNN and PyQt5

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Abstract

In today's society, breast cancer is becoming more and more serious, and the promotion of related medical means is imminent. They require modern technology support led by artificial intelligence, such as computer-aided diagnosis system for breast cancer. The existing computer-aided diagnostic systems of breast cancer have high cost, high requirements for the configuration of hospital hardware facilities, and are basically limited to the classification of benign and malignant. The paper proposes L_MiniVGGNet (improved VGGNet) and T_MobileNetV2 (improved MobileNetV2) algorithms, and a simple integrated breast cancer pathologic image pre-classification system. The system uses L_MiniVGGNet and T_MobileNetV2 as the core algorithms to solve the problem of binary and quaternary classification of pathological images, respectively. It adapts to both tensorflow and pytorch frameworks. In this paper, BreakHis and BACH open data sets are used, and data enhancement and transfer learning algorithms are combined to train high-precision and low-memory models and save them. PyQt5 library is used to build human–computer interaction GUI system interface, and images are imported according to the path required by system users and diagnosis results are predicted.

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Data availability

The data that support the findings of this study are available in https://web.inf.ufpr.br/vri/databases/breast-cancerhistopathological-database-breakhis/ and in https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathologicaldatabase-breakhis/. Both of them are public.

References

  1. Akhil, M., Kumar, PVS.: "Breast Cancer Prognosis using Machine Learning Applications," 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, pp. 488–493 (2022)

  2. Shang, M.Y., Guo, S., Zhang, Q., Pu, H.Z.: Breast cancer screening in China. J. Pract. Cancer 11, 1911–1914 (2020)

    Google Scholar 

  3. Xike, Z., Zhiqing, M., Wenhua, Z., et al.: A review of breast cancer histopathological image classification based on convolutional neural networks. Comput. Sci. 49(S2), 362–370 (2022)

    Google Scholar 

  4. Geetha, P. and Umamaheswari, S.: "Medical imaging modalities and deep learning algorithm for detecting breast cancer at the early stages," 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India pp. 1–8. (2022)

  5. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016)

    Article  Google Scholar 

  6. Hou, Y.-L.: Breast cancer pathological image classification based on deep learning. J X-ray Sci. Technol. (2020). https://doi.org/10.3233/XST-200658

    Article  Google Scholar 

  7. Khan, S., Islam, N., Jan, Z., Din, I.U., Rodrigues, J.J.P.C.: A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Patt. Recogn. Lett. 125, 1–6 (2019)

    Article  Google Scholar 

  8. Jothi, J.A.A., Damania, K.: DIRXNet: A Hybrid Deep Network for Classification of Breast Histopathology Images. SN Comput. Sci. 5, 77 (2024)

    Article  Google Scholar 

  9. Wang, Y., Sun, L., Ma, K., Fang, J.: Breast cancer microscope image classification based on CNN with image deformation. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) Image analysis and recognition iciar 2018 lecture notes in computer science. Springer, Cham (2018)

    Google Scholar 

  10. Nazeri, K., Aminpour, A., Ebrahimi, M.: Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) Image Analysis and Recognition ICIAR 2018 Lecture Notes in Computer Science. Springer, Cham (2018)

    Google Scholar 

  11. Vesal, S., Ravikumar, N., Davari, A., Ellmann, S., Maier, A.: Classification of Breast Cancer Histology Images Using Transfer Learning. In: ICIAR 2018: Image Analysis and Recognition. Springer. Cham, pp. 812–819 (2018)

  12. Loizidou, K., Elia, R., Pitris, C.: Computer-aided breast cancer detection and classification in mammography: a comprehensive review. Comput. Biol. Med. 153, 106554 (2023)

    Article  Google Scholar 

  13. Loizidou, K., Elia, R., Pitris, C.: Customer success summary: using AI technology to improve access to medical imaging diagnosis for rural health centers. Qingfeng Mi,GE Healthcare. Comput. Biol. Med. (2023). https://doi.org/10.1016/j.compbiomed.2023.106554

    Article  Google Scholar 

  14. Chuanbo, X., Qin, M., Hong, L.: The value of artificial intelligence ultrasound in the diagnosis and prognosis of breast cancer. Chin. J. Mater. Child Clin. Med. (Electronic Edition) 16(03), 368–372 (2019)

    Google Scholar 

  15. Yang, X., Yang, D., Huang, C.: An interactive prediction system of breast cancer based on ResNet50, chatbot and PyQt,", 2021 2nd International Seminar on Artificial Intelligence. Netw. Inform. Technol. (AINIT) Shanghai, China 2021, 309–316 (2021)

    Google Scholar 

  16. Simonyan, K. and Zisserman, A.: “Very Deep Convolutional Networks for Large-Scale Image Recognition.” CoRR abs/1409.1556. (2014)

  17. Ismail, A., Ahmad, S.A., Soh, A.C., et al.: Deep learning object detector using a combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and classic object detection algorithm. JST (2020). https://doi.org/10.47836/pjst.28.s2.13

    Article  Google Scholar 

  18. Roni, N.A., Hossain, M., Hossain, M.B., Efat, M.A., Yousuf, M.A.: Deep convolutional comparison architecture for breast cancer binary classification. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds.) Machine intelligence and emerging technologies MIET 2022 lecture notes of the institute for computer sciences social informatics and telecommunications engineering, pp. 1–2. Springer, Cham (2023)

    Google Scholar 

  19. Prasad, C.R., Arun, B., Amulya, S., Abboju, P., Kollem, S., Yalabaka, S.: Breast cancer classification using CNN with transfer learning models. Int. Conf. Adv. Technol. (ICONAT) Goa, India 2023, 1–5 (2023)

    Google Scholar 

  20. HAhmad, H.M., Ghuffar, S. and Khurshid, K.: "Classification of Breast Cancer Histology Images Using Transfer Learning," 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, pp. 328–332. (2019)

  21. Zhang, X., Zhou, X., Lin, M. and Sun, J.: "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 6848–6856. (2018)

  22. Howard A.G., Zhu M, Chen B, et al.: MobileNets: Efficient convolutional neural networks for mobile vision applications (2017)

  23. Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. ArXiv, abs/1602.07360 (2016)

  24. Zoph, B., Vasudevan, V., Shlens, J. and Le, Q.V.: "Learning Transferable Architectures for Scalable Image Recognition," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, , pp. 8697–8710 (2018)

  25. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. and Chen, L.C.,: "MobileNetV2: Inverted Residuals and Linear Bottlenecks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 4510–4520 (2018)

  26. Riaz, Z., Khan, B., Abdullah, S., Khan, S., Islam, M.S.: Lung tumor image segmentation from computer tomography images using MobileNetV2 and transfer learning. Bioengineering (Basel) 10(8), 981 (2023). https://doi.org/10.3390/bioengineering10080981.PMID:37627866;PMCID:PMC10451633

    Article  Google Scholar 

  27. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: "ImageNet: A large-scale hierarchical image database," 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, pp. 248–255 (2009)

  28. Qureshi, A.S., Roos, T.: Transfer learning with ensembles of deep neural networks for skin cancer detection in imbalanced data sets. Neural. Process. Lett. 55, 4461–4479 (2023)

    Article  Google Scholar 

  29. Justus, D., Brennan, J., Bonner, S., McGough, A.S.: Predicting the computational cost of deep learning models. IEEE Int. Conf. Big Data (Big Data) 2018, 3873–3882 (2018)

    Google Scholar 

  30. Zhang, H., Huang, W., Qi, J.: Design and implementation of object image detection interface system based on PyQt5 and improved SSD algorithm. IEEE 10th Joint Int. Inform. Technol. Artif. Intell. Conf. (ITAIC) Chongqing, China 2022, 2086–2090 (2022)

    Google Scholar 

  31. Kanumuri, C., Chodavarapu, R.M.: GUI enabled optimized approach of CNN for automatic diagnosis of COVID-19 using radiograph images. New Gener. Comput. 41, 213–224 (2023)

    Article  Google Scholar 

  32. Hameed, Z., Garcia-Zapirain, B., Aguirre, J.J., et al.: Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network. Sci. Rep. 12, 15600 (2022)

    Article  Google Scholar 

  33. Liang, Y., Meng, Z.: Brea-Net: an interpretable dual-attention network for imbalanced breast cancer classification. IEEE Access 11, 100508–100517 (2023)

    Article  Google Scholar 

  34. Khikani, H.A., Elazab, N., Elgarayhi, A., Elmogy, M.M., Sallah, M. Breast Cancer Classification Based on Histopathological Images Using a Deep Learning Capsule Network. ArXiv, abs/2208.00594. (2022)

  35. Sharma, S., Kumar, S.: The Xception model: a potential feature extractor in breast cancer histology images classification. ICTExpress 8, 101–108 (2022)

    Google Scholar 

  36. Saini, M., Susan, S.: VGGIN-Net: deep transfer network for imbalanced breast cancer dataset. IEEE/ACM Trans. Comput. Biol. Bioinform. 20(1), 752–762 (2023)

    Article  Google Scholar 

  37. Ijaz, A., et al.: Modality specific CBAM-VGGNet model for the classification of breast histopathology images via transfer learning. IEEE Access 11, 15750–15762 (2023)

    Article  Google Scholar 

  38. Tangsakul, S., Wongthanavasu, S.: Deep cellular automata-based feature extraction for classification of the breast cancer image. Appl. Sci. 13, 6081 (2023)

    Article  Google Scholar 

  39. Nogales, A., Pérez-Lara, F., García-Tejedor, Á.J.: Enhancing breast cancer diagnosis with deep learning and evolutionary algorithms: a comparison of approaches using different thermographic imaging treatments. Multimed. Tools Appl. (2023). https://doi.org/10.1007/s11042-023-17281-x

    Article  Google Scholar 

  40. Arooj, S., Atta-Ur-Rahman, Z.M., Khan, M.F., Alissa, K., Khan, M.A., Mosavi, A.: Breast cancer detection and classification empowered with transfer learning. Front. Public Health 10, 924432 (2022)

    Article  Google Scholar 

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Funding

This work was funded by National Natural Science Foundation of China, under Grant Nos. 61671480 and 62372468, 61671480, and 62372468, Natural Science Foundation of Shandong Province, China, under Grant Nos. ZR2019MF073, ZR2023MF008 and ZR2023ZD32, ZR2019MF073, ZR2023MF008 and ZR2023ZD32, and Qingdao Natural Science Foundation, under Grant Nos. 23-2-1-161-zyyd-jch and 23-2-1-161-zyyd-jch.

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Authors and Affiliations

  1. China University of Petroleum (East China), Tsingtao, China

    Yunkai Yang, Qijia Yang, Weifeng Liu & Baodi Liu

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  1. Yunkai Yang
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  2. Qijia Yang
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  3. Weifeng Liu
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  4. Baodi Liu
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Contributions

Y.K wrote the most part of manuscript,including drawing diagrams. Q.J made the system and wrote part of manuscript. W.F revised the manuscript. B.D supervised and managed the entire project process.

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Correspondence to Baodi Liu.

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The authors declare no conflict of interests.

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Communicated by B. Bao.

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Yang, Y., Yang, Q., Liu, W. et al. Design of integrated interactive system for pre-diagnosis of breast cancer pathological images based on CNN and PyQt5. Multimedia Systems 30, 95 (2024). https://doi.org/10.1007/s00530-024-01295-y

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