Medical Tumor Image Classification Algorithm and Its Application in Breast Cancer
Medical image classification of tumors plays an important role in the diagnosis and treatment of medical diseases. With the development of computer science and technology, medical imaging has made great progress in imaging, image acquisition speed is getting faster and faster, and image resolution is getting higher and higher. However, the interpretation of images mainly comes from imaging doctors. On the one hand, the quantity and quality of images greatly increase their burden. On the other hand, their interpretation of images mainly depends on the image characteristics that can be observed by the naked eye, which inevitably will be affected by subjective factors such as personal experience. Based on the clinical needs, this paper first uses image enhancement algorithm to extract image features, and then introduces medical tumor image classification. Finally, the paper elaborates the designed medical image classification network S-Dense Net. In this paper, the images are evaluated from subjective and objective aspects. The experimental results show that S-Dense Net has higher accuracy and AUC (Area under Curve) values than traditional algorithms including Logistic regression, LASSO logistic regression, SVM and random forest and based on Google Net, ResNet-51, and Squeeze Net network algorithms.