<![CDATA[Breast cancer continues to be a serious health issue at the global level, and early detection can significantly improve patient outcomes. This research uses imaging techniques to examine the design of an improved classification model in breast cancer detection. This project uses deep learning approaches through Convolutional Neural Networks (CNN) and ensemble learning models to potentially improve classification accuracy. To further enhance performance while controlling for class imbalance and overfitting, we leverage several models, such as ResNet18 and VGG16, with data augmentation and pre-trained models. Our methods included standard preprocessing of medical images, splitting datasets into training, testing and validation sets, and training each model with the Adam optimizer. Performance measurement included accuracy, precision, recall, and F1 score metrics. Overall, prototypes recently created displayed clear advantages based on finding results achieved through an ensemble method, which demonstrated improved model stability and reduced significant misclassification errors, and model accuracy reached 0.96. This research is crucial while developing strong deep-learning models to aid in breast cancer detection, ultimately allowing us to set a base for developing better diagnostic inference systems in medical-based applications. These systems may help improve early detection and overall patient care.]]>
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