Artificial Intelligence Models for Breast Cancer Prediction: A Comprehensive Review
DOI:
https://doi.org/10.65204/djes.v3i3.862Keywords:
Pathological Analysis, Grad-CAM, Breast cancer, Medical Image, MRI Image, MammographyAbstract
Breast cancer (BC) caused death in the worldwide, which raises the issue of the urgent essential for accurate and primary prediction. In oncology, we are seeing the rapid integration of Artificial Intelligence (AI), inclusive machine learning and deep learning, and we view this as a transformative opportunity to improve predictive analytics. This review paper looks at the present state of artificial intelligence (AI) in the prediction of breast cancer, which is the most common of all the malignancies worldwide. We examine the synthesis and analysis of recent advances, methods, and performance results for many AI models, inclusive ML and DL models. DL architectures, in particular Convolutional Neural Networks (CNNs), have achieved the best results in image-based detection tasks. Furthermore, the paper highlights that what may be the here and now in terms of performance does not diminish the value and interpretability of traditional ML models used for structured clinical and genomic data.
References
H. Alrubaie, H. K. Aljobouri, Z. J. AL-Jobawi, and I. Çankaya, “Convolutional Neural Network Deep Learning Model for Improved Ultrasound Breast Tumor Classification,” Al-Nahrain Journal for Engineering Sciences, vol. 26, no. 2, pp. 57–62, Jul. 2023, doi: 10.29194/NJES.26020057.
M. M. Neamah, L. A. Al-Ani, and L. E. George, “Deep Learning and Texture Analysis for Lung and Colon Cancer Predicting,” Iraqi Journal for Computer Science and Mathematics, vol. 6, no. 3, Sep. 2025, doi: 10.52866/2788-7421.1314.
H. A. AlSudani, E. M. Hussain, and E. A. Khalil, “Classification the Mammograms Based on Hybrid Features Extraction Techniques Using Multilayer Perceptron Classifier,” Al-Mustansiriyah Journal of Science, vol. 31, no. 4, pp. 72–79, Dec. 2020, doi: 10.23851/mjs.v31i4.902.
A. H. Abdulaal et al., “Hybrid CNN and RNN Model for Histopathological Sub-Image Classification in Breast Cancer Analysis Using Self-Learning,” Journal of Engineering and Sustainable Development, vol. 29, no. 3, pp. 310–320, May 2025, doi: 10.31272/jeasd.2746.
M. M. Neamah, A. H. Thary Al-Ghrairi, A. N. Mohammed, F. H. Jasim, and N. Abd-Alnabi, “Blood Cell Cancer Prediction Using Deep Learning-Based Models,” in Augmenting Artificial-Human Intelligence (ICAAHI 2025), Springer, Cham, 2026, pp. 319–328. doi: 10.1007/978-3-032-19130-4_26.
K. S. Priyanka, “A Review Paper on Breast Cancer Detection Using Deep Learning,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1022, no. 1, p. 012071, Jan. 2021, doi: 10.1088/1757-899X/1022/1/012071.
B. S. Abunasser, M. R. J. AL-Hiealy, I. S. Zaqout, and S. S. Abu-Naser, “Literature review of breast cancer detection using machine learning algorithms,” 2023, p. 040006. doi: 10.1063/5.0133688.
M. S. Al Reshan et al., “Enhancing Breast Cancer Detection and Classification Using Advanced Multi-Model Features and Ensemble Machine Learning Techniques,” ., vol. 13, no. 10, p. 2093, Oct. 2023, doi: 10.3390/life13102093.
Z. Q. Habeeb, B. Vuksanovic, and I. Q. Al-Zaydi, “Breast Cancer Detection Using Image Processing and Machine Learning,” Journal of Image and Graphics, vol. 11, no. 1, pp. 1–8, Mar. 2023, doi: 10.18178/joig.11.1.1-8.
K. M. M. Uddin, N. Biswas, S. T. Rikta, and S. K. Dey, “Machine learning-based diagnosis of breast cancer utilizing feature optimization technique,” Computer Methods and Programs in Biomedicine Update, vol. 3, p. 100098, 2023, doi: 10.1016/j.cmpbup.2023.100098.
M. A. A. Albadr, F. T. AL-Dhief, L. Man, A. Arram, A. H. Abbas, and R. Z. Homod, “Online sequential extreme learning machine approach for breast cancer diagnosis,” Neural Comput. Appl., vol. 36, no. 18, pp. 10413–10429, Jun. 2024, doi: 10.1007/s00521-024-09617-x.
J. Ahmad et al., “Deep learning empowered breast cancer diagnosis: Advancements in detection and classification,” PLoS One, vol. 19, no. 7, p. e0304757, Jul. 2024, doi: 10.1371/journal.pone.0304757.
A. J. Zaylaa and S. Kourtian, “Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models,” Sensors, vol. 24, no. 7, p. 2312, Apr. 2024, doi: 10.3390/s24072312.
S. Saharan, N. A. Wani, S. Chatterji, N. Kumar, and A. M. Almuhaideb, “A Deep Learning and Explainable Artificial Intelligence based Scheme for Breast Cancer Detection,” Sci. Rep., vol. 15, no. 1, p. 32125, Sep. 2025, doi: 10.1038/s41598-024-80535-7.
T. Arravalli et al., “Detection of breast cancer using machine learning and explainable artificial intelligence,” Sci. Rep., vol. 15, no. 1, p. 26931, Jul. 2025, doi: 10.1038/s41598-025-12644-w.
M. Chaieb, M. Azzouz, M. Ben Refifa, and M. Fraj, “Deep learning-driven prediction in healthcare systems: Applying advanced CNNs for enhanced breast cancer detection,” Comput. Biol. Med., vol. 189, p. 109858, May 2025, doi: 10.1016/j.compbiomed.2025.109858.
R. M. Masoud, R. M. A. Bakir, M. S. Saraya, and S. M. Ayyad, “BREAST-CAD: A Computer-Aided Diagnosis System for Breast Cancer Detection Using Machine Learning,” Technologies (Basel)., vol. 13, no. 7, p. 268, Jun. 2025, doi: 10.3390/technologies13070268.
D. Zuo, L. Yang, Y. Jin, H. Qi, Y. Liu, and L. Ren, “Machine learning-based models for the prediction of breast cancer recurrence risk,” BMC Med. Inform. Decis. Mak., vol. 23, no. 1, p. 276, Nov. 2023, doi: 10.1186/s12911-023-02377-z.
K. Atrey, B. K. Singh, and N. K. Bodhey, “Multi-Feature Classification of Breast Cancer Histopathology Images: An Experimental Investigation in Machine Learning and Deep Learning Paradigm,” Brazilian Archives of Biology and Technology, vol. 66, 2023, doi: 10.1590/1678-4324-2023220297.
M. Ebrahim, A. A. H. Sedky, and S. Mesbah, “Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer,” Data (Basel)., vol. 8, no. 2, p. 35, Feb. 2023, doi: 10.3390/data8020035.
V. Garg, S. Maggu, and B. Kapoor, “Breast Cancer Classification using Neural Networks,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 6, pp. 2272–2276, Jun. 2023, doi: 10.22214/ijraset.2023.54028.
L. H. Al Fryan, M. I. Shomo, and M. B. Alazzam, “Application of Deep Learning System Technology in Identification of Women’s Breast Cancer,” Medicina (B. Aires)., vol. 59, no. 3, p. 487, Mar. 2023, doi: 10.3390/medicina59030487.
S. Zakareya, H. Izadkhah, and J. Karimpour, “A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images,” Diagnostics, vol. 13, no. 11, p. 1944, Jun. 2023, doi: 10.3390/diagnostics13111944.
A. Rafiq et al., “Detection and Classification of Histopathological Breast Images Using a Fusion of CNN Frameworks,” Diagnostics, vol. 13, no. 10, p. 1700, May 2023, doi: 10.3390/diagnostics13101700.
W. Hanon and M. A. Salman, “Integration of ML Techniques for Early Detection of Breast Cancer: Dimensionality Reduction Approach,” Ingénierie des systèmes d information, vol. 29, no. 1, pp. 347–353, Feb. 2024, doi: 10.18280/isi.290134.
S. Devi, R. Kaul Ghanekar, J. Pande, D. Dumbre, R. Chavan, and H. Gupta, “Prediction and Diagnosis of Breast Cancer Using Machine and Modern Deep Learning Models,” Asian Pacific Journal of Cancer Prevention, vol. 25, no. 3, pp. 1077–1085, Mar. 2024, doi: 10.31557/APJCP.2024.25.3.1077.
G. Sahoo et al., “Breast cancer relapse disease prediction improvements with ensemble learning approaches,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 35, no. 1, p. 335, Jul. 2024, doi: 10.11591/ijeecs.v35.i1.pp335-342.
Mrs. S. P. Shinde and Dr. M. R. Dixit, “Deep Learning Approach for Breast Cancer Detection from Histopathology Images,” South East. Eur. J. Public Health, pp. 2383–2397, Nov. 2024, doi: 10.70135/seejph.vi.2422.
M. M. AL-NAWASHI, O. M. AL-HAZAIMEH, and M. Kh. KHAZAALEH, “NEW APPROACH FOR BREAST CANCER DETECTION- BASED MACHINE LEARNING TECHNIQUE,” Applied Computer Science, vol. 20, no. 1, Mar. 2024, doi: 10.35784/acs-2024-01.
S. Z. Hamedi et al., “Application of machine learning in breast cancer survival prediction using a multimethod approach,” Sci. Rep., vol. 14, no. 1, p. 30147, Dec. 2024, doi: 10.1038/s41598-024-81734-y.
B. Almarri, G. Gupta, R. Kumar, V. Vandana, F. Asiri, and S. B. Khan, “Correction: The BCPM method: decoding breast cancer with machine learning,” BMC Med. Imaging, vol. 24, no. 1, p. 272, Oct. 2024, doi: 10.1186/s12880-024-01451-w.
F. Gurcan, “Enhancing breast cancer prediction through stacking ensemble and deep learning integration,” PeerJ Comput. Sci., vol. 11, p. e2461, Feb. 2025, doi: 10.7717/peerj-cs.2461.
D. Kumari, M. V. S. S. Naidu, S. Panda, and J. Christopher, “Predicting breast cancer recurrence using deep learning,” Discover Applied Sciences, vol. 7, no. 2, p. 113, Jan. 2025, doi: 10.1007/s42452-025-06512-5.
A. Rhagini and S. Thilagamani, “Integration of Explainable AI with Deep Learning for Breast Cancer Prediction and Interpretability,” Information Technology and Control, vol. 54, no. 2, pp. 560–575, Jul. 2025, doi: 10.5755/j01.itc.54.2.39443.
U. K. Lilhore et al., “Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification,” Sci. Rep., vol. 15, no. 1, p. 12082, Apr. 2025, doi: 10.1038/s41598-025-95311-4.
M. Güler, G. Sart, Ö. Algorabi, A. N. Adıguzel Tuylu, and Y. S. Türkan, “Breast Cancer Classification with Various Optimized Deep Learning Methods,” Diagnostics, vol. 15, no. 14, p. 1751, Jul. 2025, doi: 10.3390/diagnostics15141751.
J. Zhu et al., “An integrated approach of feature selection and machine learning for early detection of breast cancer,” Sci. Rep., vol. 15, no. 1, p. 13015, Apr. 2025, doi: 10.1038/s41598-025-97685-x.
