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Invasive Ductal Carcinoma Classification from Whole Slide Image Based on BRISQUE and Convolutional Neural Networks

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1925))

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Abstract

This research aims to improve the performance of an invasive ductal carcinoma (IDC) classification system by examining how input image quality affects the dataset. To achieve this goal, we utilized the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) method to assess image quality and divided the dataset into subsets for training, cross-validation, and testing of the proposed Convolutional Neural Network (CNN) architecture. The study evaluated various subsets of the training dataset to determine the optimal option, which was then used to identify the appropriate training parameters and evaluate the system's performance. The results demonstrated that the proposed approaches outperformed the standard methods for IDC classification.

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Acknowledgements

This research was supported by Sai Gon University under Fund Grant No. CSB2022–39.

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Authors and Affiliations

  1. Information Science Faculty, SaiGon University, Ho Chi Minh City, Vietnam

    Le Nhi Lam Thuy, Vu Ngoc Thanh Sang, Pham The Bao & Tan Dat Trinh

Authors
  1. Le Nhi Lam Thuy
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  2. Vu Ngoc Thanh Sang
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  3. Pham The Bao
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  4. Tan Dat Trinh
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Corresponding authors

Correspondence to Pham The Bao or Tan Dat Trinh .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Industry and Trade, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Suwon-si, Korea (Republic of)

    Tai M. Chung

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Thuy, L.N.L., Sang, V.N.T., Bao, P.T., Trinh, T.D. (2023). Invasive Ductal Carcinoma Classification from Whole Slide Image Based on BRISQUE and Convolutional Neural Networks. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2023. Communications in Computer and Information Science, vol 1925. Springer, Singapore. https://doi.org/10.1007/978-981-99-8296-7_43

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  • DOI: https://doi.org/10.1007/978-981-99-8296-7_43

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8295-0

  • Online ISBN: 978-981-99-8296-7

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