<![CDATA[Breast cancer is one of the leading causes of mortality among women, creating a strong need for diagnostic methods that are accurate, consistent, and capable of handling the morphological variations present in histopathological images. This study aims to improve the stability and accuracy of breast cancer histopathology image classification through an ensemble multi-architecture Convolutional Neural Network approach. The BreakHis dataset, which consists of four magnification levels 40×, 100×, 200×, and 400× was used in this research. Three architectures, VGG19, ResNet50, and EfficientNetB0, served as the base models. All images underwent preprocessing, including resizing to 224×224 pixels, pixel-intensity normalization, and data augmentation. Each model was trained independently, and their probability outputs were combined using a soft voting mechanism to generate the final predictions. The experimental results show that the ensemble method provides the most stable and superior performance across all magnification levels. At 40× magnification, the ensemble achieved an accuracy of 92.00%, recall of 99.03%, and F1-score of 94.44%. At 100× magnification, the accuracy increased to 94.56%, with a recall of 99.07% and an F1-score of 96.18%. The 200× level produced an accuracy of 94.03%, recall of 97.61%, and an F1-score of 95.77%. Meanwhile, at 400× magnification, the model reached an accuracy of 90.11%, recall of 95.14%, and an F1-score of 92.88%. These consistently high recall and F1-score values highlight the model’s strong ability to detect malignant cases while maintaining balanced predictive performance. Overall, the findings demonstrate that combining multiple CNN architectures enhances feature representation and shows strong potential as a decision-support system for breast cancer diagnosis using histopathological images.]]>
