Comparison of DenseNet201, InceptionV3, and their fusion models in breast cancer tissue image classification

In this study, we propose a fusion model based on DenseNet201 and InceptionV3 aimed at improving the accuracy of 8-class classification of the BreakHis breast cancer histopathological image dataset. The BreakHis dataset poses significant challenges for automatic classification tasks due to its high feature similarity and class imbalance. Our model leverages the deep connectivity of DenseNet201 and the broad exploratory capabilities of InceptionV3 to capture comprehensive features from local to global scales, enhancing adaptability to various feature scales. This fusion model outperforms individual DenseNet201 or InceptionV3 models in key performance metrics such as precision, recall, and F1 score. It shows marked performance improvements, particularly in categories with high feature similarity and fewer samples, demonstrating its effectiveness in addressing internal dataset imbalances. Additionally, the model exhibits stability across multiple training and testing iterations, further validating its reliability and effectiveness in classifying breast cancer histopathological images. The results indicate that the fusion of DenseNet201 and InceptionV3 models can enhance the accuracy of automatic classification of breast cancer histopathological images, especially in categories with high feature similarity. This finding provides valuable insights for the development and selection of classification models and contributes to future work in medical image analysis.