Abstract:
Abstract: Breast cancer is one of the diseases that significantly affects women’s health， and there is currently no substantial cure for it. In recent years， with the rapid development of artificial intelligence technology， especially the potential advantages of deep learning in requiring less manual intervention during image feature extraction， this technology used in breast cancer detection to facilitate early diagnosis and personalized treatment plans， thereby improving patient survival chances. This paper selects the large publicly available breast cancer histopathological image dataset BreakHis to study breast cancer detection methods based on deep convolutional neural networks， including the application of three pre-trained models: GoogLeNet， ResNet50， and EfficientNet， constructing transfer learning techniques for both binary and multi-class classification. Experimental results show that for binary classification， the validation accuracies achieved by the above three networks are 97.01%， 97.35%， and 97.13% respectively; for multi-class classification， the validation accuracies are 87.78%， 92.33%， and 93.59% respectively， with ResNet50 showing relatively better classification performance. Finally， the challenges and future research directions of breast cancer detection based on deep learning models are analyzed.

Key words: Key words: breast cancer, disease monitoring, deep learning, transfer learning