Abstract
Nonnegative sparse representation has become a popular methodology in medical analysis and diagnosis in recent years. In order to resolve network degradation, higher dimensionality in feature extraction, data redundancy, and other issues faced when medical images parameters are trained using convolutional neural networks. Lung tumors in chest CT image based on nonnegative, sparse, and collaborative representation classification of DenseNet (DenseNet-NSCR) are proposed by this paper: firstly, initialization parameters of pretrained DenseNet model using transfer learning; secondly, training DenseNet using CT images to extract feature vectors for the full connectivity layer; thirdly, a nonnegative, sparse, and collaborative representation (NSCR) is used to represent the feature vector and solve the coding coefficient matrix; fourthly, the residual similarity is used for classification. The experimental results show that the DenseNet-NSCR classification is better than the other models, and the various evaluation indexes such as specificity and sensitivity are also high, and the method has better robustness and generalization ability through comparison experiment using AlexNet, GoogleNet, and DenseNet-201 models.
Highlights
Chest CT images offer the advantages of easy access, costeffectiveness, and low radiation dosage needed, making it the most common screening procedure in daily clinical practice
The dataset is trained in a DenseNet to construct a DenseNet model to extract the full connection layer feature vector
The DenseNet+NSCR model outperforms the other models with better robustness and generalization capabilities
Summary
Chest CT images offer the advantages of easy access, costeffectiveness, and low radiation dosage needed, making it the most common screening procedure in daily clinical practice. Deep learning, exemplified by DenseNet [4], has been increasingly applied in the field of medical imaging; good results have been achieved in clinically assisted classification, identification, detection, and segmentation for benign and malignant tumors, brain functions, cardiovascular diseases, and other major diseases. A robust sparse representation for medical image classification is proposed based on the adaptive type-2 fuzzy learning (T2-FDL) system by Ghasemi et al [19]. In order to solve the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance, Zhang et al [21] propose a multiscale nonnegative sparse coding-based medical image classification algorithm. The DenseNet+NSCR model outperforms the other models with better robustness and generalization capabilities
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