Global Cancer Observatory - Algeria, https://gco.iarc.fr/today/data/factsheets/populations/12-algeria-fact-sheets.pdf, 2020. Top 5 most frequent cancers excluding non-melanoma skin cancer, 2020.
American Cancer Society, About breast cancer, The American Cancer Society and editorial content team, 2022. URL: https://www.cancer.org/content/dam/CRC/PDF/Public/8577.00.pdf, accessed August 2022.
S. Karthik, R. Srinivasa Perumal, P. Chandra Mouli, Breast cancer classification using deep neural networks, Knowledge Computing and Its Applications: Knowledge Manipulation and Processing Techniques: Volume 1 (2018) 227–241.
R. M. Nishikawa, Current status and future directions of computer-aided diagnosis in mammography, Computerized Medical Imaging and Graphics 31 (2007) 224–235.
J. Wang, H. Ding, F. A. Bidgoli, B. Zhou, C. Iribarren, S. Molloi, P. Baldi, Detecting cardiovascular disease from mammograms with deep learning, IEEE transactions on medical imaging 36 (2017) 1172–1181.
P. Yousefikamal, Breast tumor classification and segmentation using convolutional neural networks, arXiv preprint arXiv:1905.04247 (2019).
M. A. Al-Antari, M. A. Al-Masni, T.-S. Kim, Deep learning computer-aided diagnosis for breast lesion in digital mammogram, Deep Learning in Medical Image Analysis: Challenges and Applications (2020) 59–72.
A. Bal, M. Das, S. M. Satapathy, M. Jena, S. K. Das, Automated diagnosis of breast cancer with roi detection using yolo and heuristics, in: Distributed Computing and Internet Technology: 17th International Conference, ICDCIT 2021, Bhubaneswar, India, January 7–10, 2021, Proceedings 17, Springer, 2021, pp. 253–267.
M. Z. Hanane, M. Mejdeded, Utilization of pre-trained models of cnn in mammograms processing for the diagnosis of breast cancer, in: 2022 7th International Conference on Image and Signal Processing and their Applications (ISPA), IEEE, 2022, pp. 1–5.
G. H. Aly, M. A. E.-R. Marey, S. El-Sayed Amin, M. F. Tolba, Yolo v3 and yolo v4 for masses detection in mammograms with resnet and inception for masses classification, in: Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2021, Springer, 2021, pp. 145–153.
F. A. Zeiser, C. A. da Costa, T. Zonta, N. M. Marques, A. V. Roehe, M. Moreno, R. da Rosa Righi, Segmentation of masses on mammograms using data augmentation and deep learning, Journal of digital imaging 33 (2020) 858–868.
H. Chougrad, H. Zouaki, O. Alheyane, Deep convolutional neural networks for breast cancer screening, Computer methods and programs in biomedicine 157 (2018) 19–30.
J. Shi, A technical comparison of yolo-based chest cancer diagnosis methods, Highlights in Science, Engineering and Technology 41 (2023) 35–42.
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014).
J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
G. Jocher, A. Chaurasia, A. Stoken, J. Borovec, Y. Kwon, K. Michael, J. Fang, Z. Yifu, C. Wong, D. Montes, et al., ultralytics/yolov5: v7. 0-yolov5 sota realtime instance segmentation, Zenodo (2022).
G. V. Suganthi, J. Sutha, M. Parvathy, N. Muthamil Selvi, Genetic algorithm for feature selection in mammograms for breast masses classification, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization (2023) 1–12.
M. Heath, K. Bowyer, D. Kopans, P. Kegelmeyer Jr, R. Moore, K. Chang, S. Munishkumaran, Current status of the digital database for screening mammography, in: Digital Mammography: Nijmegen, 1998, Springer, 1998, pp. 457–460.