Abstract
Cancer is one of the leading causes of death in many countries. Breast cancer is one of the most common cancers in women. Especially in remote areas with low medical standards, the diagnosis efficiency of breast cancer is extremely low due to insufficient medical facilities and doctors. Therefore, in-depth research on how to improve the diagnosis rate of breast cancer has become a hot spot. With the development of society and science, people use artificial intelligence to improve the auxiliary diagnosis of diseases in the existing medical system, which can become a solution for detecting and accurately diagnosing breast cancer. The paper proposes an auxiliary diagnosis model that uses deep learning in view of the low rate of human diagnosis by doctors in remote areas. The model uses classic convolutional neural networks, including VGG16, InceptionV3, and ResNet50 to extract breast cancer image features, then merge these features, and finally train the model VIRNets for auxiliary diagnosis. Experimental results prove that for the recognition of benign and malignant breast cancer pathological images under different magnifications, VIRNets have a high generalization and strong robustness, and their accuracy is better than their basic network and other structures of the network. Therefore, the solution provides a certain practical value for assisting doctors in the diagnosis of breast cancer in real scenes.
Highlights
Nowadays, cancer is one of the most important diseases threatening human health
According to the statistics of the World Health Organization (WHO), in 2018, there were about 2.1 million new cases of female breast cancer in the world and 1.8 million deaths, accounting for nearly a quarter of female cancer cases, which is the highest mortality of female cancer in the world [1, 2]
Effective diagnosis methods require many professional doctors or experts to carry out long-term inspections, and some remote areas or underdeveloped areas cannot adopt pathological examination methods due to insufficient doctors and equipment
Summary
Cancer is one of the most important diseases threatening human health. In more than 180 countries, cancer is the leading cause of premature death. People have proposed and Mathematical Problems in Engineering realized the application of computer vision in computeraided diagnosis [6, 7], and the application of ultrasound and pathological images in disease-aided diagnosis has become very extensive [8] erefore, it is an effective method to collect histopathological images of breast tissue through microscopic imaging and check the histopathological images through deep learning methods. Starting from the above problems, this paper uses deep learning to train and diagnose breast cancer pathological images from the perspective of delay in breast cancer diagnosis and low diagnosis accuracy, using traditional classic convolutional neural networks. (1) An auxiliary diagnosis model based on multimodel fusion convolutional neural network (VIRNets) is proposed, which achieves an average accuracy of 98.26% for the diagnosis of breast cancer pathological images (2) e adopted transfer learning strategy allows the model to extract shallow features by using the parameters of traditional natural image features, reducing model training time and actual parameters
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