A Pulmonary Tuberculosis X-ray Image Classification Model for Small Data Sets
At present,pulmonary tuberculosis is a serious threat to people's life and health.Rapid and accurate diagnosis of pulmonary tuberculosis has always been the focus and difficulty of conventional imaging studies.Due to the particularity of medical images,it is often difficult to obtain sufficient data for deep learning training.In this paper,a pulmonary tuberculosis X-ray image classification model suitable for small data sets is proposed.Firstly,image scaling,image enhancement and target region extraction are combined to preprocess the original X-ray image.After the preprocessing,feature extraction is carried out.SE-Block is added to VGG-16 network to assign weights to different channels of the image.SVM is used to classify normal X-ray and pulmonary tuber-culosis X-ray.Experimental results show that the classification accuracy of the proposed model is significantly improved compared with the baseline model and other existing models,and has better classification performance in small data sets.
small data setimage preprocessingimage classificationtransfer learningattention mechanismX-ray
国家科技期刊平台
NSTL
万方数据
