<p>Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. The integration of deep learning techniques with evolutionary algorithms presents a promising approach to improving the efficiency and accuracy of breast cancer detection systems. Current breast cancer detection methods often suffer from limitations such as high false positive rates and reliance on extensive feature engineering, which can lead to suboptimal performance. These issues highlight the need for more adaptive and efficient algorithms that can better analyze complex medical data. In response to these challenges, we propose a novel framework called Breast Cancer Detection using Deep Learning (BCD-DL), which incorporates evolutionary algorithms to optimize the deep learning process. This approach leverages the strengths of convolutional neural networks (CNNs) for feature extraction and classification, while evolutionary algorithms are employed to fine-tune model parameters and improve overall accuracy. The proposed method utilizes a convolutional neural network (CNN) to classify mammographic images, enabling the automated detection of malignancies with enhanced precision. Evolutionary algorithms play a crucial role in optimizing the architecture and hyperparameters of the CNN, resulting in improved model performance and reduced computational costs. Our findings indicate that the BCD-DL framework significantly outperforms existing evolutionary-based approaches, demonstrating a marked increase in detection accuracy and a reduction in false positives. This innovative approach not only enhances breast cancer detection but also lays the groundwork for future advancements in medical image analysis.</p>