Benign and malignant classification of renal occupancy lesions CT images using convolutional neural networks

Objective To investigate the feasibility and application value of the benign and malignant classificational methods of renal occupying CT images based on convolutional neural networks (CNN). Methods An image omics method that can automatically learn the image features and classify CT images was used. Firstly, the CNN model obtained by large-scale natural image training was used to migrate the characteristics of the renal occupancy lesions CT images, and then the fine-tuning of the full connection layer was used to realize the benign and malignant classification of the images. Results The evaluation indexes of the VGG19 model were lower than ResNet50 and Inception V3, and the training result showed obvious overfitting. The accuracy, sensitivity and negative prediction values of the Inception V3 model was 93.8%, 99.5% and 99.1%, respectively, which were higher than that of the ResNet50 model. Conclusions The benign and malignant classification of renal occupancy lesions CT images using CNN is a reasonable and feasible method, and the fine-tuned Inception V3 model has a better classification performance. Key words: Convolutional neural networks; Transfer learning; Renal occupancy lesions; CT images; Benign and malignant classification