An efficient deep learning strategy for accurate and automated detection of breast tumors in ultrasound image datasets.

Background: Breast cancer ranks as one of the leading malignant tumors among women worldwide in terms of incidence and mortality. Ultrasound examination is a critical method for breast cancer screening and diagnosis in China. However, conventional breast ultrasound examinations are time-consuming and labor-intensive, necessitating the development of automated and efficient detection models. Methods: We developed a novel approach based on an improved deep learning model for the intelligent auxiliary diagnosis of breast tumors. Combining an optimized U2NET-Lite model with the efficient DeepCardinal-50 model, this method demonstrates superior accuracy and efficiency in the precise segmentation and classification of breast ultrasound images compared to traditional deep learning models such as ResNet and AlexNet. Results: Our proposed model demonstrated exceptional performance in experimental test sets. For segmentation, the U2NET-Lite model processed breast cancer images with an accuracy of 0.9702, a recall of 0.7961, and an IoU of 0.7063. In classification, the DeepCardinal-50 model excelled, achieving higher accuracy and AUC values compared to other models. Specifically, ResNet-50 achieved accuracies of 0.78 for benign, 0.67 for malignant, and 0.73 for normal cases, while DeepCardinal-50 achieved 0.76, 0.63, and 0.90 respectively. These results highlight our model's superior capability in breast tumor identification and classification. Conclusion: The automatic detection of benign and malignant breast tumors using deep learning can rapidly and accurately identify breast tumor types at an early stage, which is crucial for the early diagnosis and treatment of malignant breast tumors.