Abstract
Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients’ survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.
Funder
y the Research Creativity
Management Office
School of Computer Sciences at the Universiti Sains Malaysia
Reference134 articles.
1. Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis;Aggarwal;Npj Digit. Med.,2021
2. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review;Yassin;Comput. Methods Programs Biomed.,2018
3. A feature-fusion framework of clinical, genomics, and histopathological data for METABRIC breast cancer subtype classification;Belal;Appl. Soft Comput.,2020
4. Breast cancer detection using artificial intelligence techniques: A systematic literature review;Nassif;Artif. Intell. Med.,2022
5. Yao, H., Zhang, X., Zhou, X., and Liu, S. (2019). Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification. Cancers, 11.
Cited by
22 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献