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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21604
Article Published : 8531
Total Authors : 22438
Total Reviewer : 805
Total Countries : 159
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Published Paper Details
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| Paper Title: | Breast Cancer Detection using CNN |
| Authors Name: | Dhara s modi |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_200165
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| Published Paper Id: | IJRTI2503079 |
| Published In: | Volume 10 Issue 3, March-2025 |
| DOI: | |
| Abstract: | Breast cancer is one of the leading causes of cancer deaths in women globally. Early and accurate detection is crucial for positive patient outcomes. In recent years, deep learning and convolutional neural networks have emerged as powerful tools for automated analysis of medical images. This review summarizes the current state of research on applying convolutional neural networks to breast cancer detection in mammography scans and histopathology images. The basics of convolutional neural networks are first introduced. Then, major network architectures used for breast cancer diagnosis, including AlexNet, VGGNet, ResNet and DenseNet are reviewed and compared. The review analyzes network performance reported in literature across different architectures and modalities. Current challenges such as class imbalance, model interpretability and data variability are discussed. Finally, future directions like multimodal learning, model compression and clinical integration are proposed to further advance the state of the art. Through extensive review of current literature, this paper aims to provide readers with a comprehensive overview of how convolutional neural networks are making strides towards automated breast cancer diagnosis and where opportunities exist to address limitations. |
| Keywords: | breast cancer; mammography; histopathology; deep learning; convolutional neural networks |
| Cite Article: | "Breast Cancer Detection using CNN", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.a618-a629, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503079.pdf |
| Downloads: | 000485 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2503079
Registration ID:200165
Published In: Volume 10 Issue 3, March-2025
DOI (Digital Object Identifier):
Page No: a618-a629 Country: Deladva, Gujarat, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2503079 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2503079 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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