| Issue |
EPJ Web Conf.
Volume 356, 2026
5th International Conference on Condensed Matter and Applied Physics (ICC 2025)
|
|
|---|---|---|
| Article Number | 01041 | |
| Number of page(s) | 8 | |
| Section | Condensed Matter | |
| DOI | https://doi.org/10.1051/epjconf/202635601041 | |
| Published online | 05 March 2026 | |
https://doi.org/10.1051/epjconf/202635601041
Breast cancer detection using an AI machine technique
1 Nims University Rajasthan Jaipur (India)
2 Tata Memorial Centre (ACTREC), Navi-Mumbai (India)
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 5 March 2026
Abstract
Early breast cancer diagnosis is important in enhancing survival rates of women with breast cancer which is one of the highest causes of cancer mortality in the world. The traditional diagnostic methods like mammography and ultrasound are very dependent on the interpretation of radiologists and have varying results, thus resulted into delayed or inaccurate diagnosis. New developments in the field of artificial intelligence (AI) are effective solutions that help increase the accuracy and consistency of diagnostics. This research paper presents a machine learning model that will be used in detecting and classifying breast cancer based on medical imaging data through the use of a convolutional neural network (CNN). The model was trained using a wide range of data (mammograms, ultrasound images and scans of magnetic resonance imaging) to differentiate between benign and malignant lesions with great accuracy. The suggested technique proves better diagnostic capability than the usual systems and minimizes the human error and clinical burden. These results suggest the possibility of AI-aided medical imaging to improve the effectiveness of the system in the detection of breast cancers at the initial stages and patient outcomes.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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