A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model

Abstract

The paper proposes an A-ResNet model to improve ResNet. The residual attention module with shortcut connection is introduced to enhance the focus on the target object; the dropout layer is introduced to prevent the overfitting phenomenon and improve the recognition accuracy; the network architecture is adjusted to accelerate the training convergence speed and improve the recognition accuracy. The experimental results show that the A-ResNet model achieves a top-1 accuracy improvement of about 2% compared with the traditional ResNet network. Image recognition is one of the core technologies of computer vision, but its application in the field of tea is relatively small, and tea recognition still relies on sensory review methods. A total of 1,713 images of eight common green teas were collected, and the modeling effects of different network depths and different optimization algorithms were explored from the perspectives of predictive ability, convergence speed, model size, and recognition equilibrium of recognition models.

A Convolutional Neural Network for Automated Detection of Cervical Ossification of the Posterior Longitudinal Ligament using Magnetic Resonance Imaging

“…25 The input images of the ResNet model are processed through a convolutional layer, a max-pooling layer, and several residual blocks, then with an average polling layer and fully connected layer, finally result in an output vector of class probabilities. 26 Kazuma and colleagues performed a study using ResNet12 to identify cervical OPLL based on x-rays, they reported a great accuracy of 98.9%. 15 We evaluated the performance of ResNet models with 34-layer, 50-layer, and 101-layer of residual blocks in detecting OPLL with MRI, and we obtained a higher accuracy in those with more residual connection layers.…”

“…The ResNet won the ImageNet Large Scale Visual Recognition Challenge by training deeper networks while maintaining fast convergence times 25 . The input images of the ResNet model are processed through a convolutional layer, a max-pooling layer, and several residual blocks, then with an average polling layer and fully connected layer, finally result in an output vector of class probabilities 26 . Kazuma and colleagues performed a study using ResNet12 to identify cervical OPLL based on x-rays, they reported a great accuracy of 98.9% 15 .…”

Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

“…The test results will be stored and will be used for evaluation and conclusions at a later stage. [35]. ResNet 9 architecture, which has 9 layers, can be seen in Figure 3.…”

Retracted: A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”