Convolutional neural network-based breast image diagnosis and analysis system
Jianing Cao, Pengtao Zhu · 2023
In order to achieve accurate screening and effective diagnosis of breast at early stage and thus improve the treatment rate of breast cancer patients, this paper proposes a convolutional neural network-based breast image diagnosis and analysis system, which aims to achieve rapid and accurate detection and diagnosis of early breast cancer through digital mammogram impact and breast pattern processing and digital feature recognition. The system uses convolutional neural network algorithm for classification, segmentation and diagnosis of breast images, which has the advantages of high accuracy, high efficiency and automation. In this paper, the algorithm principle, experimental method and experimental results of the system are introduced and analyzed, and compared with other breast image diagnosis and analysis systems. Finally the system provides real-time display and image recognition display of the results of diagnostic analysis, which is easy for personnel to view and modify. The experimental results show that the system has an accuracy rate of over 90% for the specified types of breast abnormalities affected and can be used as an important tool for diagnostic analysis of breast images.