Abstract
Deep learning is a subfield of machine learning and plays a vital role in the area of image processing, natural language processing, computer vision, etc. As compared to traditional machine learning methods, it has a strong ability of self-learning and self-debugging. Convolution neural network (CNN) is the most widely used technique of deep learning for better feature extraction from large datasets. Many researchers adopted CNN for object classification, face recognition, automatic handwritten, etc. In this paper, the detailed concepts behind CNN are discussed with their broad applications.
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References
Du, X., Cai, Y., Wang, S., Zhang, L.: Overview of deep learning. In: 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 159–164. Wuhan (2016)
Kido, S., Hirano, Y., Hashimoto, N.: Detection and classification of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN). In: International Workshop on Advanced Image Technology (IWAIT), pp. 1–4. Chiang Mai (2018)
Lei, X., Pan, H., Huang, X.: A dilated CNN model for image classification. IEEE Access 7, 124087–124095 (2019)
Lu, L., Yi, Y., Huang, F., Wang, K., Wang, Q.: Integrating local CNN and global CNN for script identification in natural scene images. IEEE Access 7, 52669–52679 (2019)
Wang, X., Gao, L., Song, J., Shen, H.: Beyond frame-level CNN: saliency-aware 3-D CNN with LSTM for video action recognition. IEEE Signal Process. Lett. 24(4), 510–514 (2017)
Jiang, J., Feng, X., Liu, F., Xu, Y., Huang, H.: Multi-spectral RGB-NIR image classification using double-channel CNN. IEEE Access 7, 20607–20613 (2019)
Qu, D., Huang, Z., Gao, Z., Zhao, Y., Zhao, X., Song, G.: An automatic system for smile recognition based on CNN and face detection. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 243–247. Kuala Lumpur, Malaysia (2018)
Ma, L., Bai, L.: A face detection algorithm based on Adaboost and new haar-like feature. IEEE Chifeng University Chi/eng, Inner Mongolia Autonomous. China (2016)
Wan, L., Chen, P.: Face detection method based on skin color and AdaBoost algorithm. In: Fourth International Conference on Computational and Information Sciences. Chengdu, Sichuan China (2010)
Teow, M.Y.W.: Understanding convolutional neural networks using a minimal model for handwritten digit recognition. In: IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), pp. 167–172. Kota Kinabalu (2017)
Han, M., Chen, J., Li, L., Chang, Y.: Visual hand gesture recognition with convolution neural network. In: 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 287–291. Shanghai, China (2016)
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Sahu, M., Dash, R. (2021). A Survey on Deep Learning: Convolution Neural Network (CNN). In: Mishra, D., Buyya, R., Mohapatra, P., Patnaik, S. (eds) Intelligent and Cloud Computing. Smart Innovation, Systems and Technologies, vol 153. Springer, Singapore. https://doi.org/10.1007/978-981-15-6202-0_32
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DOI: https://doi.org/10.1007/978-981-15-6202-0_32
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