Image recognition is an important direction in computer vision. After years of research, image recognition technology has made great progress. This paper mainly studies the application of convolutional neural network (CNN) in image recognition. This paper first analyzes the basic mechanism of CNN, and on this basis, uses genetic algorithm to optimize CNN for image recognition application. Experimental results show that the optimal connection structure searched by this method can significantly improve the effect of CNN image recognition and speed up the training speed of the model.

Mayank Abdullah. Convolutional Neural Network in Image Recognition. International Journal of Neural Network (2021), Vol. 2, Issue 2: 33-39. https://doi.org/10.38007/NN.2021.020205.

[1] Campilho A, Karray F, Bart T. [Lecture Notes in Computer Science] Image Analysis and Recognition Volume 10882 || Fully CNN for Lungs Segmentation from Chest X-Rays. (2018) 10.1007/978-3-319-93000-8(Chapter 9):71-80. https://doi.org/10.1007/978-3-319-93000-8_9

[2] Vidushi, Agarwal M, Rajak A, et al. Assessment of Optimizers Impact on Image Recognition with CNN to Adversarial Datasets. Journal of Physics: Conference Series. (2021) 1998(1):012008. https://doi.org/10.1088/1742-6596/1998/1/012008

[3] Sharan R V, Moir T J. Acoustic Event Recognition Using Cochleagram Image and Cnns. Applied Acoustics. (2019) 148(MAY):62-66. https://doi.org/10.1016/j.apacoust.2018.12.006

[4] Dat N, Ki K, Hong H, et al. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a CNN for Image Feature Extraction. Sensors. (2017) 17(3):637. https://doi.org/10.3390/s17030637

[5] Uddin M Z, Khaksar W, Torresen J. Facial Expression Recognition Using Salient Features and CNN. IEEE Access. (2017) (99):1-1. https://doi.org/10.1109/ACCESS.2017.2777003

[6] Siregar B, Wirtjes J S, Nababan E B, et al. Development of a Person's Facial Expression Recognition System Using a CNN. Journal of Physics: Conference Series. (2021) 1882(1):012127 (10pp). https://doi.org/10.1088/1742-6596/1882/1/012127

[7] Joshi S, Kumar R, Dwivedi A. Hybrid DSSCS and CNN for Peripheral Blood Cell Recognition System. IET Image Processing. (2020) 14(17):4450-4460. https://doi.org/10.1049/iet-ipr.2020.0370

[8] Natarajan, Sudha, Baradkar, et al. Traffic Sign Recognition Using Weighted Multi-CNN. IET Intelligent Transport Systems. (2018) 12(10):1396-1405. https://doi.org/10.1049/iet-its.2018.5171

[9] Tomimori H, Chen K T, Baba T. A CNN with Incremental Learning. Journal of Signal Processing. (2017) 21(4):155-158. https://doi.org/10.2299/jsp.21.155

[10] Paoletti M E, Haut J M, Plaza J, et al. A New Deep CNN for Fast Hyperspectral Image Classification. Isprs Journal of Photogrammetry & Remote Sensing. (2017) 145PA (NOV.):120-147. https://doi.org/10.1016/j.isprsjprs.2017.11.021

[11] Abtahi T, Shea C, Kulkarni A, et al. Accelerating CNN with FFT on Embedded Hardware. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. (2018) (99):1-24.

[12] Clark T, Nyberg E. Creating the Black Box: A Primer on CNN Use in Image Interpretation - ScienceDirect. Current Problems in Diagnostic Radiology. (2020) 49(6):365-367. https://doi.org/10.1067/j.cpradiol.2019.07.004

[13] Chandio A A, Asikuzzaman M, Pickering M R. Cursive Character Recognition in Natural Scene Images using a Multilevel CNN Fusion. IEEE Access. (2020) (99):1-1. https://doi.org/10.1109/ACCESS.2020.3001605

[14] Lvarez-Canchila O I, DE Arroyo-Pérez, A Patiňo-Saucedo, et al. Colombian Fruit and Vegetables Recognition Using Cnns and Transfer Learning. Journal of Physics: Conference Series. (2020) 1547(1):01(2020) (6pp). https://doi.org/10.1088/1742-6596/1547/1/012020

[15] Kwak Y, Song W J, Kim S E. Speckle-Noise-Invariant CNN for SAR Target Recognition. IEEE Geoscience and Remote Sensing Letters. (2019) 16(4):549-553. https://doi.org/10.1109/LGRS.2018.2877599

[16] Rivera P, Anazco E V, Choi M T, et al. Trilateral CNN for 3D Shape Reconstruction of Objects From A Single Depth View. Image Processing, IET. (2019) 13(13):2457-2466. https://doi.org/10.1049/iet-ipr.2019.0532

[17] Jaffe L, Zelinski M, Sakla W. Remote Sensor Design for Visual Recognition with CNNs. IEEE Transactions on Geoscience and Remote Sensing. (2019) 57(11):9090-9108. https://doi.org/10.1109/TGRS.2019.2925813

[18] Hirasawa T, Aoyama K, Tanimoto T, et al. Application of Artificial Intelligence Using a CNN for Detecting Gastric Cancer in Endoscopic Images. Gastric Cancer Official Journal of the International Gastric Cancer Association & the Japanese Gastric Cancer Association. (2018) 87(Suppl 1):1-8.