Breast Cancer Detection with 1D Convolutional Neural Networks

Breast cancer continues to be one of the leading causes of mortality among women globally, emphasizing the urgent need for effective diagnostic tools to ensure early detection and treatment. In this study, we investigate using convolutional neural networks (CNNs) to enhance the accuracy and efficiency of breast cancer classification. Using the Wisconsin Breast Cancer Dataset, a benchmark dataset in breast cancer research, we designed and trained a CNN-based model capable of distinguishing between malignant and benign cases. Our approach incorporates advanced deep learning techniques to maximize diagnostic accuracy, including data preprocessing, model optimization, and performance evaluation. The proposed model achieved a classification accuracy of 97.3%, outperforming several existing methods in similar contexts. These results highlight the potential of CNNs as reliable and scalable components of computer-aided diagnosis systems. Moreover, the integration of such AI-driven models into clinical workflows can significantly aid healthcare professionals in early breast cancer detection, ultimately improving patient outcomes and reducing healthcare burdens. This study serves as a foundation for future research to expand datasets, enhance model robustness, and address real-world clinical challenges.