| dc.description.abstract | One of the most prevalent illnesses affecting
women worldwide is breast cancer. It increases in
countries where the majority of cases are discovered in
the late stages. The machine learning (ML) technique
that is used in this paper to detect breast cancer is
retrieved from a digitized mammogram image. It Aimed
to evaluate and compare the performance of various
machine-learning algorithms such as Convolutional
Neural Networks (CNN), Random Forest, Support Vector
Machine (SVM), Logistic Regression, and K-Nearest
Neighbors (KNN) for breast cancer detection. Using a
comprehensive dataset of "RSNA Screening
Mammography Breast Cancer Detection", these
mammographic images and clinical information are
divided into training and testing phases to implement the
ML algorithms. The objective was to determine which
algorithm yielded the highest accuracy in predicting
breast cancer, as this is a critical factor in early detection
and successful treatment. research highlights the
Convolutional Neural Network (CNN) gives 95.2%
accuracy as the most effective machine learning
algorithm for breast cancer prediction. CNN’s ability to
learn intricate patterns from mammographic images and
its superior accuracy make it a valuable tool in early
breast cancer detection. These findings have significant
implications for improving patient outcomes and the
overall effectiveness of breast cancer screening and
diagnosis. CNNs revolutionize computer vision,
enabling accurate breast cancer diagnosis and detection
through automatic learning and feature identification in
medical imaging tasks. website's backend will employ
the algorithm that produces the best results, and the
model will categorize cancer as benign or malignant. | en_US |
