¹College of Electronic and Information Engineering, Southwest University, Chongqing, China.
²College of Computer and Information Science, Southwest University, Chongqing, China.
³College of Computer Science and Engineering, Sichuan University of Science and Engineering, Sichuan Zigong, China.
⁴Informatics Institute, University of Alabama at Birmingham America, Birmingham.

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Abstract—In recent years there have been many successes of using deep learning for imaging classification recognition. In this work firstly we discuss in details the differences between machine learning and deep learning from the limitations of traditional machine learning, and gives a detail introduction to the advantages of typical deep convolution neural network in image classification. Deeper neural networks are more difficult to train, this paper presents an improving deep learning convolutional neural network (CNN) structure model and gain accuracy from considerably increased depth. We also show that this improving structure model leads to the prediction results are higher than the original deep-learning CNN structure model with training and testing on the published data set.

Index Terms—Machine-learning, convolutional neural network, image classification, structure model.

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Cite: Ming Ye, Zhisai Shi, Guangyuan Liu, "An Improved CNN Structure Model for Image Classification Recognition," Journal of Computers vol. 13, no. 12, pp. 1349-1356, 2018.