International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Driven Breast Cancer Classification Using Mammographic Image Analysis |
|---|---|
| ABSTRACT | By looking at how societal stigmas and unfair treatment of genders make it hard to find breast cancer early this research is a first step towards solving the problems with finding breast cancer. It does this by checking all the things that can affect breast cancer like pathways and the actual disease, and uses lots of data from places like CBIS-DDSM, SEER and BreakHis to give important information about breast cancer images. The research uses a type of intelligence called deep learning specifically the MobileNetV2 system with something called transfer learning, which is a new and exciting technology. The results show that the system can tell the difference between harmful breast cancer cases pretty well even though its hard to do with a total accuracy score of 0.616. The system is also good at being fair and precise for harmless cases which makes it useful for doctors and hospitals. By showing how deep learning can help make tools for finding breast cancer and change how we find breast cancer this research gives us a strong base, for future improvements. It helps us understand breast cancer and breast cancer detection better. The use of learning in breast cancer detection is very helpful.Breast cancer detection can be improved with learning. The model learns to tell the difference between bad things by training and checking itself over and over. This process helps the model get really good at knowing what is what. The people who wrote the paper found out that the deep learning model is better than mammography at finding breast cancer. It is more sensitive and specific which means it can find the things more easily and not get too many false results. The deep learning model is really good, at detecting breast cancer, which’s a big deal. |
| AUTHOR | SINCHANA S, DR.GIRISH L M. Tech, Department of CSE, SIET Tumkur, India Associate Professor & HOD Department of AI & DS, SIET Tumkur, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406009 |
| pdf/9_AI-Driven Breast Cancer Classification Using Mammographic Image Analysis.pdf | |
| KEYWORDS | |
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