A Review of Image Processing Pipelines and Deep Learning Methods for Breast Cancer Diagnosis Using Medical Imaging
Keywords:
Artificial intelligence, Breast cancer, CNN, Deep learning, Diagnosis, Machine learning, Mammography, Medical imagingAbstract
It has long been a normal procedure in the field of medical computing to employ computer-aided image analysis to enhance picture understanding. Breast cancer ranks among the most common and fatal cancers in women worldwide. A timely and precise diagnosis is therefore necessary to improve the prognosis for individuals with breast cancer. Breast cancer is a major problem in the modern world since it is one of the main causes of cancer-related deaths worldwide. To identify breast cancer, this study provides a comprehensive analysis of both conventional and advanced imaging techniques, including digital breast tomosynthesis, PET, ultrasound, MRI, image-guided biopsy, mammography, and histopathological imaging. Diagnostic accuracy has greatly increased as deep learning and Artificial Intelligence (AI) have become more prevalent. Examine how image processing pipelines that use ML and DL models such as Convolutional Neural Networks (CNNs), AlexNet, and Artificial Neural Networks (ANNs) incorporate feature extraction, classification, preprocessing, and segmentation tasks. The study outlines the benefits of these AI-powered methods for improving early detection while also pointing out current issues with interpretability, model generalization, and data quality.
References
K. Munir, H. Elahi, A. Ayub, F. Frezza, and A. Rizzi, “Cancer diagnosis using deep learning: A bibliographic review,” Cancers, vol. 11, no. 9, p. 1235, Aug. 2019, doi: https://doi.org/10.3390/cancers11091235
S. A. Qureshi et al., “Breast cancer detection using mammography: Image processing to deep learning,” IEEE Access, pp. 1–1, 2024, doi: https://doi.org/10.1109/access.2024.3523745
M. A. Mostafiz, “Machine learning for early cancer detection and classification: AI-based medical imaging analysis in healthcare,” Int. J. Curr. Eng. Technol., vol. 15, no. 3, pp. 251– 260, 2025, Available: https://inpressco.com/machine-learning-for-early-cancer-detection-and-classification-ai-based-medical-imaging-analysis-in-healthcare/
V. D. P. Jasti et al., “Computational technique based on machine learning and image processing for medical image analysis of breast cancer diagnosis,” Security and Communication Networks, vol. 2022, p. e1918379, Mar. 2022, doi: https://doi.org/10.1155/2022/1918379
Q. Hu, H. M. Whitney, and M. L. Giger, “A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI,” Scientific Reports, vol. 10, no. 1, p. 10536, Jun. 2020, doi: https://doi.org/10.1038/s41598-020-67441-4
M. S. K. Inan, F. I. Alam, and R. Hasan, “Deep integrated pipeline of segmentation guided classification of breast cancer from ultrasound images,” Biomedical Signal Processing and Control, vol. 75, p. 103553, May 2022, doi: https://doi.org/10.1016/j.bspc.2022.103553
Rahul Dattangire, Divya Biradar, Leelkanth Dewangan, and Ashish Joon, “Unlocking healthcare fraud detection using innovations of machine learning strategies,” Learning and Analytics in Intelligent Systems, pp. 233–249, Jan. 2025, doi: https://doi.org/10.1007/978-981-97-9855-1_16
M. Tsuneki, “Deep learning models in medical image analysis,” Journal of Oral Biosciences, vol. 64, no. 3, Mar. 2022, doi: https://doi.org/10.1016/j.job.2022.03.003
Rahul P. Mahajan, “Optimizing pneumonia identification in chest X-rays using deep learning pre-trained architecture for image reconstruction in medical imaging,” International Journal of Advanced Research in Science, Communication and Technology, pp. 52–63, Apr. 2025, doi: https://doi.org/10.48175/ijarsct-24808
R. Gerami et al., “A literature review on the imaging methods for breast cancer,” International Journal of Physiology, Pathophysiology and Pharmacology, vol. 14, no. 3, p. 171, Jun. 2022, Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC9301184/
Suhag Pandya, “A machine and deep learning framework for robust health insurance fraud detection and prevention,” International Journal of Advanced Research in Science, Communication and Technology, pp. 1332–1342, Jul. 2023, doi: https://doi.org/10.48175/ijarsct-14000u
M. Madani, M. M. Behzadi, and S. Nabavi, “The role of deep learning in advancing breast cancer detection using different imaging modalities: A systematic review,” Cancers, vol. 14, no. 21, p. 5334, Oct. 2022, doi: https://doi.org/10.3390/cancers14215334
Y. Mo et al., “HoVer-Trans: Anatomy-aware HoVer-transformer for ROI-free breast cancer diagnosis in ultrasound images,” IEEE Transactions on Medical Imaging, vol. 42, no. 6, pp. 1696–1706, Jun. 2023, doi: https://doi.org/10.1109/tmi.2023.3236011
X. Jiang, Z. Hu, S. Wang, and Y. Zhang, “Deep learning for medical image-based cancer diagnosis,” Cancers, vol. 15, no. 14, pp. 3608–3608, Jul. 2023, doi: https://doi.org/10.3390/cancers15143608
Rahul P. Mahajan, “Transfer learning for MRI image reconstruction: Enhancing model performance with pretrained networks,” International Journal of Science and Research Archive, vol. 15, no. 1, pp. 298–309, Apr. 2025, doi: https://doi.org/10.30574/ijsra.2025.15.1.0939
P. Prasad Jalaja, D. Kommineni, A. Mishra, R. Tumati, C. Anna Joseph, and R. Veer Samara Sihman Bharattej Rupavath, “Predictors of mortality in acute myocardial infarction: Insights from the Healthcare Cost and Utilization Project (HCUP) Nationwide Readmission Database,” Cureus, May 2025, doi: https://doi.org/10.7759/cureus.83675
D. A. Zebari, H. Haron, S. R. M. Zeebaree, and D. Q. Zeebaree, “Enhance the mammogram images for both segmentation and feature extraction using wavelet transform,” in 2019 International Conference on Advanced Science and Engineering (ICOASE), IEEE, Apr. 2019, pp. 100–105. doi: 10.1109/ICOASE.2019.8723779
Sreenivas Reddy Sagili, Senthilnathan Chidambaranathan, Nagarjuna Nallametti, H. M. Bodele, L. Raja, and P. G. Gayathri, “NeuroPCA: Enhancing Alzheimer’s disorder disease detection through optimized feature reduction and machine learning,” pp. 1–9, Jul. 2024, doi: https://doi.org/10.1109/iceeict61591.2024.10718628
D. A. Zebari et al., “Systematic review of computing approaches for breast cancer detection based on computer aided diagnosis using mammogram images,” Applied Artificial Intelligence, vol. 35, no. 15, pp. 1–47, Dec. 2021, doi: https://doi.org/10.1080/08839514.2021.2001177
S. B. Shah, “Artificial intelligence (AI) for brain tumor detection: Automating MRI image analysis for enhanced accuracy,” International Journal of Current Engineering and Technology, vol. 14, no. 06, Dec. 2024, doi: https://doi.org/10.14741/ijcet/v.14.5.5
H. Dhahri, E. Al Maghayreh, A. Mahmood, W. Elkilani, and M. Faisal Nagi, “Automated breast cancer diagnosis based on machine learning algorithms,” Journal of Healthcare Engineering, vol. 2019, pp. 1–11, Nov. 2019, doi: https://doi.org/10.1155/2019/4253641
G. Forrai et al., “Use of diagnostic imaging modalities in modern screening, diagnostics and management of breast tumours 1st Central-Eastern European Professional Consensus Statement on Breast Cancer,” Pathology and Oncology Research, vol. 28, Jun. 2022, doi: https://doi.org/10.3389/pore.2022.1610382
S. Pandya, “Predictive modeling for cancer detection based on machine learning algorithms and AI in the healthcare sector,” Int. Res. J., vol. 11, no. 12, pp. 549–555, 2024.
S. J. S. Gardezi, A. Elazab, B. Lei, and T. Wang, “Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review,” Journal of Medical Internet Research, vol. 21, no. 7, p. e14464, Jul. 2019, doi: https://doi.org/10.2196/14464
S. M. Shah, R. A. Khan, S. Arif, and U. Sajid, “Artificial intelligence for breast cancer analysis: Trends & directions,” Computers in Biology and Medicine, vol. 142, p. 105221, Mar. 2022, doi: https://doi.org/10.1016/j.compbiomed.2022.105221
R. Dattangire, D. Biradar, R. Vaidya, and A. Joon, “A comprehensive analysis of cholera disease prediction using machine learning,” Lecture Notes in Networks and Systems, pp. 555–568, 2025, doi: https://doi.org/10.1007/978-981-96-2697-7_39
A. Carriero, Léon Groenhoff, Elizaveta Vologina, P. Basile, and M. Albera, “Deep learning in breast cancer imaging: State of the art and recent advancements in early 2024,” Diagnostics, vol. 14, no. 8, pp. 848–848, Apr. 2024, doi: https://doi.org/10.3390/diagnostics14080848
Nithya Shankar S, Adithya R, I. R. Oviya, Vaishnavi S, Veena Sundari A, and A. Rajan, “Deep learning-based multimodal breast cancer detection,” pp. 1–6, May 2025, doi: https://doi.org/10.1109/iccrtee64519.2025.11052913
C. Luo, X. Wu, K. Feng, D. Ma, L. Fang, and B. Qiu, “Attention-driven multi-sequence MRI feature learning and fusion for breast cancer diagnosis,” 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), pp. 1–5, Apr. 2025, doi: https://doi.org/10.1109/isbi60581.2025.10981203
A. F. Valencia-Duque, D. A. Cardenas-Peña, J. Gil-González, Á. Orozco-Gutiérrez and G. Daza-Santacoloma, “Breast cancer tissue classification from multiple annotators using chained deep learning approaches,” 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 2024, pp. 1-4, doi: https://doi.org/10.1109/EMBC53108.2024.10782427
A. R. Dandekar, A. Sharma, and J. Mishra, “A deep learning and feature optimization-based approach for early breast cancer detection,” 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–7, Feb. 2024, doi: https://doi.org/10.1109/sceecs61402.2024.10482104
S. Ardabili, Amirhosein Mosavi, and I. Felde, “Advances of deep learning in breast cancer modeling,” pp. 000501–000508, Sep. 2023, doi: https://doi.org/10.1109/sisy60376.2023.10417961
S. Gengtian, B. Bing, and Z. Guoyou, “Efficient Net-based deep learning approach for breast cancer detection with mammography images,” IEEE Xplore, Apr. 01, 2023. Available: https://ieeexplore.ieee.org/document/10151156
Shah Harsh Manishkumar and P Saranya, “Detection and classification of breast cancer from mammogram images using adaptive deep learning technique,” 2022 6th International Conference on Devices, Circuits and Systems (ICDCS), pp. 327–331, Apr. 2022, doi: https://doi.org/10.1109/icdcs54290.2022.9780770
S. S. Boudouh and M. Bouakkaz, “Breast cancer: Using deep transfer learning techniques Alexnet convolutional neural network for breast tumor detection in mammography images,” IEEE Xplore, May 01, 2022. https://ieeexplore.ieee.org/document/9786351
