Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset.

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  • Additional Information
    • Source:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • Subject Terms:
      Breast Neoplasms*/diagnostic imaging ; Breast Neoplasms*/diagnosis ; Breast Neoplasms*/pathology ; Deep Learning* ; Mammography*/methods ; Databases, Factual*; Humans ; Female ; Wisconsin ; Neural Networks, Computer ; Early Detection of Cancer/methods
    • Abstract:
      The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset-a revised variant of the Digital Database for Screening Mammography (DDSM)-offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes.
      (© 2024. The Author(s).)
    • References:
      IEEE Trans Med Imaging. 2020 Apr;39(4):1184-1194. (PMID: 31603772)
      Radiology. 2019 Jul;292(1):60-66. (PMID: 31063083)
      Comput Biol Med. 2021 Apr;131:104248. (PMID: 33631497)
      Comput Methods Programs Biomed. 2021 Mar;200:105913. (PMID: 33422854)
      Neural Netw. 2023 May;162:240-257. (PMID: 36913821)
      Methods. 2020 Feb 15;173:52-60. (PMID: 31212016)
      Comput Intell Neurosci. 2022 Jun 24;2022:8141530. (PMID: 35785076)
      Nat Med. 2021 Feb;27(2):244-249. (PMID: 33432172)
      Comput Biol Med. 2022 Mar;142:105205. (PMID: 35065408)
      Sci Rep. 2019 Aug 29;9(1):12495. (PMID: 31467326)
      Radiology. 2019 Apr;291(1):23-30. (PMID: 30777808)
      Sci Rep. 2020 May 14;10(1):7991. (PMID: 32409756)
      Sensors (Basel). 2022 Jan 21;22(3):. (PMID: 35161552)
      Biomed Opt Express. 2020 Jun 09;11(7):3673-3683. (PMID: 33014559)
      JAMA Intern Med. 2019 May 1;179(5):658-667. (PMID: 30882843)
      Med Image Anal. 2021 Jul;71:102049. (PMID: 33901993)
      Diagnostics (Basel). 2022 Feb 21;12(2):. (PMID: 35204646)
      Biomimetics (Basel). 2023 Apr 17;8(2):. (PMID: 37092415)
      Diagnostics (Basel). 2023 Apr 18;13(8):. (PMID: 37189556)
      JAMA Intern Med. 2019 Sep 1;179(9):1292-1295. (PMID: 31233086)
      Comput Biol Med. 2023 Feb;153:106554. (PMID: 36646021)
      AJR Am J Roentgenol. 2019 Dec;213(6):1397-1402. (PMID: 31553658)
      Diagnostics (Basel). 2023 Sep 12;13(18):. (PMID: 37761292)
      NPJ Breast Cancer. 2021 Dec 2;7(1):151. (PMID: 34857755)
      Sensors (Basel). 2022 Nov 15;22(22):. (PMID: 36433415)
    • Contributed Indexing:
      Keywords: AUC-ROC; CBIS-DDSM; CNN; Wisconsin Breast Cancer Database
    • Publication Date:
      Date Created: 20241102 Date Completed: 20241102 Latest Revision: 20241104
    • Publication Date:
      20241104
    • Accession Number:
      PMC11530441
    • Accession Number:
      10.1038/s41598-024-74305-8
    • Accession Number:
      39487199