Breast Cancer Detection
28 papers with code • 4 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in Breast Cancer Detection
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Datasets
BRACS
CBIS-DDSM
BCI
BreakHis
BreastClassifications4
Latest papers with no code
Technical outlier detection via convolutional variational autoencoder for the ADMANI breast mammogram dataset
The ADMANI datasets (annotated digital mammograms and associated non-image datasets) from the Transforming Breast Cancer Screening with AI programme (BRAIx) run by BreastScreen Victoria in Australia are multi-centre, large scale, clinically curated, real-world databases.
Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography
However, after controlling for confounding, we found lower FN risk associates with Other race(RR=0. 828;p=. 050), biopsy-proven benign lesions(RR=0. 927;p=. 011), and mass(RR=0. 921;p=. 010) or asymmetry(RR=0. 854;p=. 040); higher FN risk associates with architectural distortion (RR=1. 037;p<. 001).
Breast cancer detection using deep learning
Results: NASNetLarge is the best architecture which can be used for the CNN model with accuracy of 88. 41% and loss of 27. 82%.
A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images
Breast cancer is one of the most common and dangerous cancers in women, while it can also afflict men.
Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning
Automatic identification of patients with luminal and non-luminal subtypes during a routine mammography screening can support clinicians in streamlining breast cancer therapy planning.
Investigation of Applying Quantum Neural Network of Early-Stage Breast Cancer Detection
Breast cancer is a fatal disease that can be treated successfully if it is detected early.
Improving the diagnosis of breast cancer based on biophysical ultrasound features utilizing machine learning
In this study, we propose a biophysical feature based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide a color overlay visual map of the probability of malignancy within a lesion.
Machine Learning Approaches to Predict Breast Cancer: Bangladesh Perspective
This study focuses on finding the best algorithm that can forecast breast cancer with maximum accuracy in terms of its classes.
A Combined PCA-MLP Network for Early Breast Cancer Detection
Breast cancer is the second most responsible for all cancer types and has been the cause of numerous deaths over the years, especially among women.
Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography
A major limitation in applying deep learning to artificial intelligence (AI) systems is the scarcity of high-quality curated datasets.
