A Comparative Study of Deep Learning Architectures for Image Classification

  • Authors

    • M. Riyaz Mohammed Assistant professor, Department of Computer Science and Information Technology, Jamal Mohammed College Autonomous), Tiruchirappalli, Tamil Nadu, India. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2020PI7T1K

    Published 01-03-2020

  • Deep Learning, Image Classification, Convolutional Neural Networks, Residual Networks, Dense Connectivity, Model Comparison, Computer Vision

    Issue

    Vol. 3 No. 1 (2020)

    Section

    Articles

    How to Cite

    [1]
    R. Mohammed, “A Comparative Study of Deep Learning Architectures for Image Classification”, IJADSMC, vol. 3, no. 1, pp. 01–14, Jan. 2020, doi: 10.67228/30713498/IJADSMC-2020PI7T1K.

  • Abstract

    Image classification is a major area in computer vision, driven by rapid advances in deep learning. Over the last decade, convolutional neural networks (CNNs) and their variants have achieved high performance in applications such as medical diagnosis, autonomous driving, industrial inspection, remote sensing, and biometrics. However, choosing the right model remains challenging due to trade-offs between accuracy, computational cost, efficiency, and robustness. This paper presents a comparative study of different deep learning architectures, including classical CNNs, deep hierarchical models, residual and dense networks, and compound-scaled architectures. Using a common evaluation framework and standard datasets, the study analyzes performance based on key design factors such as depth, width, receptive field, skip connections, and normalization. Theoretical concepts like convolution operations, residual learning, and optimization are also discussed.The results show that deeper networks provide better representation, while residual connections and compound scaling improve training stability and efficiency. Lightweight models perform well in resource-limited and real-time environments. Overall, the study offers practical guidance for selecting suitable architectures and highlights future research areas such as neural architecture search, self-supervised learning, and efficient model deployment.

  • References

    [1] Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems (NeurIPS), vol. 25, pp. 1097–1105, 2012.

    [2] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Computation, vol. 1, no. 4, pp. 541–551, 1989.

    [3] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” International Conference on Learning Representations (ICLR), 2015.

    [4] Szegedy et al., “Going deeper with convolutions,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, 2015.

    [5] R. He, J. Sun, and X. Tang, “Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification,” IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034, 2015.

    [6] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.

    [7] K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” European Conference on Computer Vision (ECCV), pp. 630–645, 2016.

    [8] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708, 2017.

    [9] G. Huang, S. Liu, L. van der Maaten, and K. Q. Weinberger, “CondenseNet: An efficient DenseNet using learned group convolutions,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2752–2761, 2018.

    [10] G. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.

    [11] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510–4520, 2018.

    [12] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1251–1258, 2017.

    [13] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” Proceedings of the 36th International Conference on Machine Learning (ICML), pp. 6105–6114, 2019.

    [14] S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding,” International Conference on Learning Representations (ICLR), 2016.

    [15] J. Deng et al., “ImageNet: A large-scale hierarchical image database,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248–255, 2009.

  • Downloads