Lung tumor image recognition algorithm with densenet fusion multi-scale images

To address the problem of inadequate feature extraction and high feature dimension when CT modal medical images are trained with convolutional neural networks, this paper proposes a method for lung tumor identification using Multi-scale DenseNet-NSCR based on non-negative sparse collaborative representation classification by fusing multi-scale images. First, the parameters of the pre-trained dense neural network model are initialized using migration learning; the lung images are then pre-processed to extract multi-scale lesion ROI. Subsequently, the DenseNet is trained using a multi-scale CT dataset to extract feature vectors at the full connection layer. To address the problem of the high dimensionality of the fused features, a non-negative, sparse, and collaborative representation (NSCR) classifier is used to represent the feature vector and solve the coefficient matrix; the residual similarity is then used for classification. Finally, a comparison test is conducted with the AlexNet, DenseNetNetNet-201 model, and a combination model of three classification algorithms (SVM, SRC, NSCR). The experimental results show that Multiscale-DenseNet-NSCR classification is better than other models; all evaluation indexes such as specificity and sensitivity are higher, and the method has better robustness and generalization ability. © 2021, Science Press. All right reserved.