Chinese Journal of Intelligent Science and Technology ›› 2023, Vol. 5 ›› Issue (1): 58-68.doi: 10.11959/j.issn.2096-6652.202307

• Papers and Reports • Previous Articles     Next Articles

Application of Fit CutMix data augmentation algorithm based on saliency information in medical images

Xinhuan LUO1,2, Yixuan WANG1,2, Wei LI1,2, Xi CHEN1,2   

  1. 1 School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
    2 Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China
  • Revised:2023-03-03 Online:2023-03-15 Published:2023-03-01
  • Supported by:
    The National Natural Science Foundation of China(61473131)


Deep convolutional neural network is one of the mainstream algorithms in the field of image classification, but its training requires a large number of labeled data, which leads to over fitting on small datasets such as Alzheimer's medical images.Data augmentation can increase the amount of training data, and CutMix data augmentation algorithm has been widely used recently.However, the augmented images generated by the CutMix series methods often ignore the significant area of the original image, and the design of the label of the augmented image takes only single factor into consideration.In order to solve these problems, the Fit CutMix data augmentation algorithm was proposed.Firstly, the region replacement strategy based on the transfer of saliency extreme value was used to generate augmented samples, so as to concentrate the regions with high saliency value in the source samples and target samples.Secondly, the area and saliency information of the source samples and the target samples were combined to assign the augmented sample label, which provided effective supervision information for the convolutional neural network.The experimental results showed that when Fit CutMix was used in ResNet50 to diagnose Alzheimer's disease, the accuracy was 96.6%, which was about 7% higher than that of directly using ResNet50, and at least 3% higher than that of applying existing methods.Therefore, the Fit CutMix data augmentation algorithm can effectively improve the recognition accuracy of deep convolutional neural network for medical images.

Key words: data augmentation, convolutional neural network, Alzheimer's disease, saliency detection

CLC Number: 

No Suggested Reading articles found!