DEEP LEARNING BASED BREAST CANCER CLASSIFICATION USING CNN MODELS

This study investigates the application of deep learning techniques, specifically Convolutional Neural Networks (CNNs), for breast cancer classification using medical imaging data. The research focuses on evaluating different CNN architectures, analyzing the impact of data preprocessing methods, and optimizing training parameters to improve classification performance. The findings indicate that advanced CNN models significantly enhance diagnostic accuracy and reliability compared to traditional methods. Furthermore, the study highlights the importance of evaluation metrics such as precision, recall, and F1-score in assessing model effectiveness. The results demonstrate that CNN-based systems can serve as efficient decision-support tools in clinical practice, contributing to early detection and improved patient outcomes.