Breast Cancer Detection: A Comprehensive Study on Machine Learning and Deep Learning Techniques

Breast cancer is one of the leading causes of cancer-related mortality among women worldwide. Early detection is crucial for improving survival rates and treatment outcomes. This paper explores various machine learning (ML) and deep learning (DL) techniques for breast cancer detection, utilizing the publicly available Wisconsin Breast Cancer Dataset. The study evaluates the performance of algorithms such as Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN). Results indicate that while traditional ML methods achieve accuracies up to 96.5%, deep learning approaches, particularly ANN, can reach an accuracy of 99.3%.