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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.

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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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