This work addresses the critical need for the early detection of breast cancer, a significant health concern worldwide. Using a combination of advanced deep learning and machine learning techniques, we offer a comprehensive solution to enhance breast cancer detection accuracy. By leveraging state-of-the-art convolutional neural networks (CNNs) like GoogLeNet, AlexNet, and ResNet18, alongside traditional classifiers such as k-nearest neighbors (KNN) and support vector machine (SVM), we ensure robust prediction capabilities. Our preprocessing methods significantly improve input data quality, leading to promising detection accuracies. For instance, ResNet-18 achieved impressive results, outperforming other models. Furthermore, our integration of these algorithms into a user-friendly MATLAB R2024b application ensures easy access for medical professionals, facilitating timely diagnosis and treatment. This work represents a vital step towards more effective breast cancer diagnosis, underscoring the importance of early intervention for improved patient outcomes.

This work addresses the critical need for early detection of breast cancer, a significant health concern worldwide. Through a combination of advanced deep learning and machine learning techniques, we offer a comprehensive solution to enhance breast cancer detection accuracy. By leveraging state-of-the-art convolutional neural networks (CNNs) like GoogLeNet, AlexNet, and ResNet18, alongside traditional classifiers such as k-nearest neighbors (KNN) and support vector machine (SVM), we ensure robust prediction capabilities. Our preprocessing methods significantly improve input data quality, leading to promising detection accuracies. For instance, ResNet-18 achieved impressive results, outperforming other models. Furthermore, our integration of these algorithms into a user-friendly MATLAB application ensures easy access for medical professionals, facilitating timely diagnosis and treatment. This work represents a vital step towards more effective breast cancer diagnosis, underscoring the importance of early intervention for improved patient outcomes.