objectives: Aimed to establish and validate deep learning model based on dynamic continuous image of mammary gland. Methods: 506 cases of female breast ultrasound were performed

real-time dynamic images were stored

and the images were imported into the intelligent analysis platform of deep learning. The end-to-end tumor detection network based on deep learning was used to analyze and extract the original dynamic sequence images

and the optimized deep learning model was trained and established

and the validity of the model was tested and verified. Data were analyzed using Python 3.6 software. Results: The sensitivity of single frame breast ultrasound images (0.1

0.2

0.5/scan) were 76.6%

84.2%

86.0%

and the sensitivity of sequential breast ultrasound images (0.1

0.2

0.5/scan) were 77.3%

91.8%

95.3%. At 0.1/scan

there was no statistical significance in lump detection between single frame breast ultrasound image and serial breast ultrasound image(P>0.05)

At 0.2/scan

there was statistical significance in lump detection between single frame breast ultrasound image and serial breast ultrasound image (P<0.05)

At 0.5/scan

there was statistical significance in lump detection between single frame breast ultrasound image and serial breast ultrasound image (P<0.05). Conclusions: Deep learning model based on dynamic continuous breast ultrasound image can improve the breast tumor detection rate.