Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study
This work addresses the need for improved precision and efficiency in breast cancer diagnosis in clinical settings, but it is incremental as it compares existing models without introducing new methods.
This study tackled the problem of classifying histopathological images for early and accurate breast cancer detection by comparing eight deep learning models, with the Vision Transformer (ViT) achieving the highest validation accuracy of 94%.
This study evaluates the effectiveness of deep learning models in classifying histopathological images for early and accurate detection of breast cancer. Eight advanced models, including ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet, were compared using a dataset of 277,524 image patches. The Vision Transformer (ViT) model, with its attention-based mechanisms, achieved the highest validation accuracy of 94%, outperforming conventional CNNs. The study demonstrates the potential of advanced machine learning methods to enhance precision and efficiency in breast cancer diagnosis in clinical settings.