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作者:

ur Rehman, Khalil (ur Rehman, Khalil.) | Li, Jianqiang (Li, Jianqiang.) | Pei, Yan (Pei, Yan.) | Yasin, Anaa (Yasin, Anaa.) | Ali, Saqib (Ali, Saqib.)

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摘要:

Breast cancer is the most deadly cancer in females globally. Architectural distortion is the third most often reported irregularity on digital mammograms among the masses and microcalcification. Physically identifying architectural distortion for radiologists is problematic because of its subtle appearance on the dense breast. Automatic early identification of breast cancer using computer algorithms from a mammogram may assist doctors in eliminating unwanted biopsies. This research presents a novel diagnostic method to identify AD ROIs from mammograms using computer vision-based depth-wise CNN. The proposed methodology is examined on private PINUM 2885 and public DDSM 3568 images and achieved a 0.99 and 0.95 sensitivity, respectively. The experimental findings revealed that the proposed scheme outperformed SVM, KNN, and previous studies. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词:

Image classification Support vector machines Deep learning X ray screens Mammography Diseases

作者机构:

  • [ 1 ] [ur Rehman, Khalil]School of Software Engineering, Beijing University of Technology, Beijing, China
  • [ 2 ] [Li, Jianqiang]School of Software Engineering, Beijing University of Technology, Beijing, China
  • [ 3 ] [Li, Jianqiang]Beijing Engineering Research Center for IoT Software and Systems, Beijing, China
  • [ 4 ] [Pei, Yan]Computer Science Division, University of Aizu, Aizuwakamatsu, Japan
  • [ 5 ] [Yasin, Anaa]School of Software Engineering, Beijing University of Technology, Beijing, China
  • [ 6 ] [Ali, Saqib]School of Software Engineering, Beijing University of Technology, Beijing, China

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ISSN: 1876-1100

年份: 2022

卷: 935 LNEE

页码: 3-14

语种: 英文

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SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

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