References
- Adam, R., DelľAquila, K., Hodges, L., Maldjian, T., Duong, T. Q. (2023). Deep learning applications to breast cancer detection by magnetic resonance imaging: A literature review. Breast Cancer Research, 25 (1), 87.
http://dx.doi.org/10.1186/s13058-023-01687-4 - Ahmad, J., Akram, S., Jaffar, A., Ali, Z., Bhatti, S. M., Ahmad, A., Rehman, S. U. (2024). Deep learning empowered breast cancer diagnosis: Advancements in detection and classification. PLOS One, 19 (7), e0304757.
http://dx.doi.org/10.1371/journal.pone.0304757 - Zheng, J., Lin, D., Gao, Z., Wang, S., He, M., Fan, J. (2020). Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis. IEEE Access, 8, 96946–96954.
http://dx.doi.org/10.1109/access.2020.2993536 - Mohamed, E. A., Rashed, E. A., Gaber, T., Karam, O. (2022). Deep learning model for fully automated breast cancer detection system from thermograms. PLOS One, 17 (1), e0262349.
http://dx.doi.org/10.1371/journal.pone.0262349 - Wang, X., Ahmad, I., Javeed, D., Zaidi, S. A., Alotaibi, F. M., Ghoneim, M. E., Daradkeh, Y. I., Asghar, J., Eldin, E. T. (2022). Intelligent hybrid deep learning model for breast cancer detection. Electronics, 11 (17), 2767.
http://dx.doi.org/10.3390/electronics11172767 - Sharmin, S., Ahammad, T., Talukder, M. A., Ghose, P. (2023). A hybrid dependable deep feature extraction and ensemble-based machine learning approach for breast cancer detection. IEEE Access, 11, 87694–87708.
http://dx.doi.org/10.1109/access.2023.3304628 - Hamed, G., El-Rahman Marey, M. A., El-Sayed Amin, S., Tolba, M. F. (2020). Deep learning in breast cancer detection and classification. In Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). Springer, 322–333.
http://dx.doi.org/10.1007/978-3-030-44289-7_30 - Khalid, A., Mehmood, A., Alabrah, A., Alkhamees, B. F., Amin, F., AlSalman, H., Choi, G. S. (2023). Breast cancer detection and prevention using machine learning. Diagnostics, 13 (19), 3113.
http://dx.doi.org/10.3390/diagnostics13193113 - Shermila, P. J., Ahilan, A., Shunmugathammal, M., Marimuthu, J. (2023). DEEPFIC: Food item classification with calorie calculation using dragonfly deep learning network. Signal, Image and Video Processing, 17 (7), 3731–3739.
http://dx.doi.org/10.1007/s11760-023-02600-4 - Rose, R. A. M., Gopalakrishnanand, A., Vasuki, J. (2024). Deep learning based LSTM-GAN approach for intrusion detection in cloud environment. International Journal of Data Science and Artificial Intelligence, 2 (3), 68–73.
- Sreelekshmi, P. G., Babu, P. L., Shermila, P. J. (2023). Leukemia classification using a fusion of transfer learning and support vector machine. International Journal of Current Bio-Medical Engineering, 1 (1), 1–8.
- Suh, Y. J., Jung, J., Cho, B.-J. (2020). Automated breast cancer detection in digital mammograms of various densities via deep learning. Journal of Personalized Medicine, 10 (4), 211.
http://dx.doi.org/10.3390/jpm10040211 - Dinesh, J. S. R., Fenil, E., Gunasekaran, M., Vivekananda, G. N., Thanjaivadivel, T., Jeeva, S., Ahilan, A. (2019). Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM. Computer Networks, 151, 191–200.
http://dx.doi.org/10.1016/j.comnet.2019.01.028 - Ramani, R., Vimala Devi, K., Thiruselvan, P., Umamaheswari, M. (2024). Classification of liver cancer via deep learning based dilated attention convolutional neural network. International Journal of Data Science and Artificial Intelligence, 2 (4), 128–134.
- Rahman, M. M., Jahangir, M. Z. B., Rahman, A., Akter, M., Al Nasim, M. A., Gupta, K. D., George, R. (2024). Breast cancer detection and localizing the mass area using deep learning. Big Data and Cognitive Computing, 8 (7), 80.
http://dx.doi.org/10.3390/bdcc8070080 - Abunasser, B. S., Al-Hiealy, M. R. J., Zaqout, I. S., Abu-Naser, S. S. (2023). Convolution neural network for breast cancer detection and classification using deep learning. Asian Pacific Journal of Cancer Prevention, 24 (2), 531–544.
http://dx.doi.org/10.31557/apjcp.2023.24.2.531 - Raza, A., Ullah, N., Khan, J. A., Assam, M., Guzzo, A., Aljuaid, H. (2023). DeepBreastCancerNet: A novel deep learning model for breast cancer detection using ultrasound images. Applied Sciences, 13 (4), 2082.
http://dx.doi.org/10.3390/app13042082 - Sharmin, S., Ahammad, T., Talukder, M. A., Ghose, P. (2023). A hybrid dependable deep feature extraction and ensemble-based machine learning approach for breast cancer detection. IEEE Access, 11, 87694–87708.
http://dx.doi.org/10.1109/access.2023.3304628 - Singh, S., Kumar, R. (2022). Breast cancer detection from histopathology images with deep inception and residual blocks. Multimedia Tools and Applications, 81 (4), 5849–5865.
http://dx.doi.org/10.1007/s11042-021-11775-2 - Liu, M., Hu, L., Tang, Y., Wang, C., He, Y., Zeng, C., Lin, K., He, Z., Huo, W. (2022). A deep learning method for breast cancer classification in the pathology images. IEEE Journal of Biomedical and Health Informatics, 26 (10), 5025–5032.
http://dx.doi.org/10.1109/jbhi.2022.3187765 - Mohanakurup, V., Parambil Gangadharan, S. M., Goel, P., Verma, D., Alshehri, S., Kashyap, R., Malakhil, B. (2022). Breast cancer detection on histopathological images using a composite dilated Backbone Network. Computational Intelligence and Neuroscience.
http://dx.doi.org/10.1155/2022/8517706 - Hirra, I., Ahmad, M., Hussain, A., Ashraf, M. U., Saeed, I. A., Qadri, S. F., Alghamdi, A. M., Alfakeeh, A. S. (2021). Breast cancer classification from histopathological images using patch-based deep learning modeling. IEEE Access, 9, 24273–24287.
http://dx.doi.org/10.1109/access.2021.3056516 - Hameed, Z., Zahia, S., Garcia-Zapirain, B., Aguirre, J. J., Vanegas, A. M. (2020). Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors, 20 (16), 4373.
http://dx.doi.org/10.3390/s20164373 - Kaushal, C., Kaushal, K., Singla, A. (2021). Firefly optimization-based segmentation technique to analyse medical images of breast cancer. International Journal of Computer Mathematics, 98 (7), 1293–1308.
http://dx.doi.org/10.1080/00207160.2020.1817411 - Wang, W.-C., Han, Z.-J., Zhang, Z., Wang, J. (2025). Enhancing sand cat swarm optimization based on multi-strategy mixing for solving engineering optimization problems. Evolutionary Intelligence, 18, 7.
https://doi.org/10.1007/s12065-024-00996-7 - Hamza, M. A., Mengash, H. A., Nour, M. K., Alasmari, N., Aziz, A. S. A., Mohammed, G. P., Zamani, A. S., Abdelmageed, A. A. (2022). Improved bald eagle search optimization with synergic deep learning-based classification on breast cancer imaging. Cancers, 14 (24), 6159.
http://dx.doi.org/10.3390/cancers14246159