Published on in Vol 7, No 3 (2019): Jul-Sep

Preprints (earlier versions) of this paper are available at, first published .
Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review

Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review

Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review

Authors of this article:

Rafia Masud 1 Author Orcid Image ;   Mona Al-Rei 1 Author Orcid Image ;   Cynthia Lokker 1 Author Orcid Image


  1. Zhang Y, Moy A, Feng X, Nguyen H, Sebastian K, Reichenberg J, Wilke C, Markey M, Tunnell J. Assessment of Raman Spectroscopy for Reducing Unnecessary Biopsies for Melanoma Screening. Molecules 2020;25(12):2852 View
  2. George‐Jones N, Wang K, Wang J, Hunter J. Automated Detection of Vestibular Schwannoma Growth Using a Two‐Dimensional U‐Net Convolutional Neural Network. The Laryngoscope 2021;131(2) View
  3. Richard A, Mayag B, Talbot F, Tsoukias A, Meinard Y. What does it mean to provide decision support to a responsible and competent expert?. EURO Journal on Decision Processes 2020;8(3-4):205 View
  4. Toz G, Erdogmus P. A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW). IEEE Access 2021;9:85377 View
  5. Zhou K, Li W, Zhao D. Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3+. Technology and Health Care 2022;30:173 View
  6. Kumar P, Kumar A, Srivastava S, Padma Sai Y. A novel bi-modal extended Huber loss function based refined mask RCNN approach for automatic multi instance detection and localization of breast cancer. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2022;236(7):1036 View
  7. Suradi S, Abdullah K, Isa N. Automated Classification of Breast Cancer Lesions for Digitised Mammograms via Computer-Aided Diagnosis System. Journal of Applied Science & Process Engineering 2021;8(2):892 View
  8. Guo Z, Xie J, Wan Y, Zhang M, Qiao L, Yu J, Chen S, Li B, Yao Y. A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis. Open Life Sciences 2022;17(1):1600 View
  9. Cellina M, Cè M, Khenkina N, Sinichich P, Cervelli M, Poggi V, Boemi S, Ierardi A, Carrafiello G. Artificial Intelligence in the Era of Precision Oncological Imaging. Technology in Cancer Research & Treatment 2022;21:153303382211417 View
  10. Ng C. Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review. Children 2023;10(3):525 View
  11. Patel K, Huang S, Rashid A, Varghese B, Gholamrezanezhad A. A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer. Life 2023;13(10):2011 View
  12. Kunar M, Watson D. Framing the fallibility of Computer-Aided Detection aids cancer detection. Cognitive Research: Principles and Implications 2023;8(1) View
  13. Retson T, Eghtedari M. Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning’s Role in Breast Imaging beyond Screening Mammography. Diagnostics 2023;13(13):2133 View
  14. Malik M, Yasmin S, Kumar A, Hassan Y, Rizvi Y, . I. Can Artificial Intelligence Beat Humans in Detecting Breast Malignancy on Mammograms?. Cureus 2023 View
  15. Lokaj B, Pugliese M, Kinkel K, Lovis C, Schmid J. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. European Radiology 2023 View

Books/Policy Documents

  1. Gadde S. Holistic Approach to Breast Disease. View