As the most prevalent subtype of breast cancer, Invasive Ductal Carcinoma (IDC) poses significant challenges in early detection and diagnosis. This study introduces a computer-aided diagnosis system that effectively detects IDC from histopathology images, utilizing advanced machine learning and deep learning techniques. A key feature of this system is the implementation of a sliding window approach for generating high-resolution heatmaps across whole slide images, allowing for precise localization of IDC-positive regions. The model's convolutional neural network architecture is optimized through hyperparameter tuning, and it employs image stitching and heatmap overlay techniques to ensure clear and interpretable visual outputs. These enhancements enable pathologists to better understand the model's predictions. The proposed system achieved a balanced accuracy of 89.06% and an F1-score of 86.68%, surpassing existing models in the literature. These findings demonstrate the potential of the sliding window approach and imaging techniques to significantly enhance diagnostic accuracy, positioning the model as a valuable tool for clinical practice and improving patient outcomes in IDC detection.