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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM60295

MammoAI – Breast Cancer Detection

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM60295

Volume:

10 |

4 |

April 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem60295

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

LM

Latika Madankar

VR

Virti Ramesh

TM

Tanvi Mane

ST

Sagar Tambe

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Abstract

Millions of new cases of breast cancer are diagnosed each year, making it one of the biggest global health issues. Although timely diagnosis is still hampered by limited access to qualified radiologists and diagnostic Resources, early and accurate detection significantly improve, treatment outcomes. In order to help radiologist detect and interpret cases of breast cancer this study presents MammoAI, an intelligent system that combines Artificial Intelligence (AI) and Natural Language Processing (NLP). Convolutional Neural Networks (CNNs) are used by the AI model to analyze mammograms automatically and accurately detect possible cancers. The NLP module bridges the communication gap between patients and medical professionals by simultaneously interpreting complex radiology reports and producing summaries that are easy for patients to understand. Through interpretable AI explanations, experimental          analysis          shows                 that MammoAI not only improves diagnostic efficiency but also         fosters   patient   understanding.   The   system demonstrates how AI has the potential to revolutionize health communication and medical imaging.

KEYWORDS

Radiology Deep Leaming Artificial Intell1gence Breast Cancer Detection Natural Language Processing Healthcare Technology

Cite This Article

Latika Madankar, Virti Ramesh, Tanvi Mane, Sagar Tambe, "MammoAI – Breast Cancer Detection," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, April 2026. https://doi.org/10.55041/ijsrem60295

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Publication Date

4/15/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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