Version 1
: Received: 24 May 2023 / Approved: 26 May 2023 / Online: 26 May 2023 (03:50:59 CEST)
How to cite:
Mohammadi, S.; Ahmadi Livani, M. A Review of CAD systems for Breast Mass Detection in Mammography Based on Deep Learning. Preprints2023, 2023051832. https://doi.org/10.20944/preprints202305.1832.v1
Mohammadi, S.; Ahmadi Livani, M. A Review of CAD systems for Breast Mass Detection in Mammography Based on Deep Learning. Preprints 2023, 2023051832. https://doi.org/10.20944/preprints202305.1832.v1
Mohammadi, S.; Ahmadi Livani, M. A Review of CAD systems for Breast Mass Detection in Mammography Based on Deep Learning. Preprints2023, 2023051832. https://doi.org/10.20944/preprints202305.1832.v1
APA Style
Mohammadi, S., & Ahmadi Livani, M. (2023). A Review of CAD systems for Breast Mass Detection in Mammography Based on Deep Learning. Preprints. https://doi.org/10.20944/preprints202305.1832.v1
Chicago/Turabian Style
Mohammadi, S. and Mohammad Ahmadi Livani. 2023 "A Review of CAD systems for Breast Mass Detection in Mammography Based on Deep Learning" Preprints. https://doi.org/10.20944/preprints202305.1832.v1
Abstract
Early detection of breast cancer, one of the most common cancers in women worldwide, raises survival rates and lowers the cost of treatment. Although breast cancer detection and classification (CAD) tools have improved, some issues and restrictions still require further research. The creation of breast cancer CAD systems, particularly with deep learning models, was significantly impacted by recent advancements in machine learning and image processing techniques. The current deep learning-based CAD system to identify and categorize masses in mammography is presented in a structured manner in this survey. The survey provides the most popular breast cancer CAD system evaluation metrics, publicly available mammographic datasets, and current image modalities. The survey examines the available literature, emphasizing its strengths and limitations. The survey also emphasizes the difficulties and drawbacks of the current methods for classifying and detecting breast cancer. We point out research gaps and make suggestions for further study. This systematic review may be beneficial for clinicians using CAD systems to diagnose breast cancer early on and for researchers looking for knowledge gaps and making more contributions to the field.
Keywords
Breast cancer; convolution neural networks; computer-aided diagnosis systems; segmentation; classification
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
