Research of image recognition method based on enhanced inception-ResNet-V2

• Published in: Multimedia tools and applications Vol. 81; no. 24; pp. 34345 - 34365
• Main Authors: Peng, Cheng, Liu, Yikun, Yuan, Xinpan, Chen, Qing
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2022; Springer Nature B.V
• Subjects: 1168: Deep Pattern Discovery for Big Multimedia Data; Accuracy; Algorithms; Artificial neural networks; Breast cancer; Classification; Comparative studies; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Feature extraction; Image classification; Image enhancement; Multimedia; Multimedia Information Systems; Neural networks; Object recognition; Special Purpose and Application-Based Systems; Deep learning; Feature extraction; Convolutional neural network; Image classification
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-12387-0

In order to improve the accuracy of CNN (convolutional neural network) in image classification, an enhanced Inception-ResNet-v2 model based on CNN is designed through the comparative study and analysis of the structure of classification model. This paper proposes to use multi-scale depthwise separable convolution to replace the convolution structure in Inception-ResNet-v2 model, which can reduce the amount of model parameters and extract features under different receptive fields. At the same time, this paper establishes channel filtering module based on global information comparison to filter and join channels, which realizes the effective extraction of features. Finally, through data enhancement, batch normalization and learning rate adjustment, the effect of the model used in this paper is better than most other models in each dataset, and the accuracy rate can reach 94.8%.