Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM44376
Volume:
9 |
4 |
Apr 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
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.
â Globally, breast cancer is one of the main causes of death for women. Improved survival rates and successful treatment depend on early and precise identification. Although manual analysis of histopathological pictures is time-consuming and subject to variation among pathologists, these images offer crucial diagnostic information. Using histopathology pictures, we provide a deep learning-based method in this article to automatically detect breast cancer. To identify benign and malignant instances, we use Convolutional Neural Networks (CNNs) to extract characteristics. The suggested model outperforms more established machine learning methods like Support Vector Machines (SVM) in terms of accuracy after being trained on the Break His dataset. According to our findings, deep learning can greatly improve the precision and effectiveness of breast cancer diagnosis.
KEYWORDS
Publication Date
4/12/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
0
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...