A comparison of conventional and deep learning methods of image classification

MNIST handwritten digit database, http://yann.lecun.com/exdb/mnist [13.02.2021]
  Google Scholar

Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient–based learning applied to document recognition, Proceedings of the IEEE 86(11) (1998) 2278–2324.
DOI: https://doi.org/10.1109/5.726791   Google Scholar

S. B. Driss, M. Soua, R. Kachouri, M. Akil, A comparison study between MLP and convolutional neural network models for character recognition, in SPIE Conference on RealTime Image and Video Processing, Anaheim, United States, 10–11 April (2017) 1022306.
DOI: https://doi.org/10.1117/12.2262589   Google Scholar

N. Sharma, V. Jain, A. Mishra, An analysis of convolutional neural networks for image classification, Procedia computer science 132 (2018) 377–384.
DOI: https://doi.org/10.1016/j.procs.2018.05.198   Google Scholar

J. M. Peña, P. A. Gutiérrez, C. Hervás–Martínez, J. Six, R. E. Plant, F. López–Granados, Object–based image classification of summer crops with machine learning methods, Remote Sensing 6(6) (2014) 5019–5041.
DOI: https://doi.org/10.3390/rs6065019   Google Scholar

D. X. Zhou, Universality of deep convolutional neural networks, Applied and computational harmonic analysis 48(2) (2020) 787–794.
DOI: https://doi.org/10.1016/j.acha.2019.06.004   Google Scholar

I. M. Dheir, A. S. A. Mettleq, A. A. Elsharif, S. S. Abu–Naser, Classifying Nuts Types Using Convolutional Neural Network, International Journal of Academic Information Systems Research 3(12) (2020) 12–18.
  Google Scholar

Y. Li, J. Nie, X. Chao, Do we really need deep CNN for plant diseases identification?, Computers and Electronics in Agriculture 178 (2020) 105803.
DOI: https://doi.org/10.1016/j.compag.2020.105803   Google Scholar

P. Sharma, Y. P. S. Berwal, W. Ghai, Performance analysis of deep learning CNN models for disease detection in plants using image segmentation, Information Processing in Agriculture 7(4) (2019) 566–574.
DOI: https://doi.org/10.1016/j.inpa.2019.11.001   Google Scholar

J. G. A. Barbedo, Impact of data set size and variety on the effectiveness of deep learning and transfer learning for plant disease classification, Computers and electronics in agriculture 153 (2018) 46–53.
DOI: https://doi.org/10.1016/j.compag.2018.08.013   Google Scholar

P. T. T. Ngo, M. Panahi, K. Khosravi, O. Ghorbanzadeh, N. Karimnejad, A. Cerda, S. Lee, Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran, Geoscience Frontiers 12(2) (2020) 505–519.
DOI: https://doi.org/10.1016/j.gsf.2020.06.013   Google Scholar

I. Banerjee, Y. Ling, M. C. Chen, S. A. Hasan, C. P. Langlotz, N. Moradzadeh, B. Chapman, T. Amrhein, D. Mong, D. L. Rubin, O. Farri, M. P. Lungren, Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification, Artificial intelligence in medicine 97 (2019) 79–88.
DOI: https://doi.org/10.1016/j.artmed.2018.11.004   Google Scholar

C. L. Chowdhary, M. Mittal, P. A. Pattanaik, Z. Marszalek, An efficient segmentation and classification system in medical images using intuitionist possibilistic fuzzy C–mean clustering and fuzzy SVM algorithm, Sensors 20(14) (2020) 3903.
DOI: https://doi.org/10.3390/s20143903   Google Scholar

T. Nakaura, T. Higaki, K. Awai, O. Ikeda, Y. Yamashita, A primer for understanding radiology articles about machine learning and deep learning, Diagnostic and Interventional Imaging 101(12) (2020) 763–844.
DOI: https://doi.org/10.1016/j.diii.2020.10.001   Google Scholar

X. Yang, Y. Ye, X. Li, R. Y. Lau, X. Zhang, X. Huang, Hyperspectral image classification with deep learning models., IEEE Transactions on Geoscience and Remote Sensing 56(9) (2018) 5408–5423.
DOI: https://doi.org/10.1109/TGRS.2018.2815613   Google Scholar

A. F. Agarap, An architecture combining convolutional neural network (CNN) and support vector machine (SVM) for image classification, arXiv:1712.03541v2 (2019).
  Google Scholar

Y. Sun, B. Xue, M. Zhang, G. G. Yen, J. Lv, Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification, IEEE Transactions on Cybernetics 50(9) (2020) 3840–3854.
DOI: https://doi.org/10.1109/TCYB.2020.2983860   Google Scholar

F. Sultana, A. Sufian, P. Dutta, Evolution of image segmentation using deep convolutional neural network: A survey, Knowledge–Based Systems 201–202 (2020) 106062.
DOI: https://doi.org/10.1016/j.knosys.2020.106062   Google Scholar

O. Sbai, C. Couprie, M. Aubry, Impact of base data set design on few–shot image classification, in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, United Kingdom, August 23–28 (2020) 597–613.
DOI: https://doi.org/10.1007/978-3-030-58517-4_35   Google Scholar

C. Gambella, B. Ghaddar, J. Naoum–Sawaya, Optimization problems for machine learning: a survey, European Journal of Operational Research 290(3) (2020) 807–828.
DOI: https://doi.org/10.1016/j.ejor.2020.08.045   Google Scholar

Y. Wang, Y. Li, Y. Song, X. Rong, The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition, Applied Sciences 10(5) (2020) 1897.
DOI: https://doi.org/10.3390/app10051897   Google Scholar

D. Bashir, G. D. Montanez, S. Sehra, P. S. Segura, J. Lauw, An Information–Theoretic Perspective on Overfitting and Underfitting, in AI 2020: Advances in Artificial Intelligence: 33rd Australasian Joint Conference, Canberra, Australia, November 29–30 (2020) 347–358.
DOI: https://doi.org/10.1007/978-3-030-64984-5_27   Google Scholar

T. Kiran, Computer Vision Accuracy Analysis with Deep Learning Model Using TensorFlow, International Journal of Innovative Research in Computer Science & Technology (IJIRCST) 8(4) (2020) 2347–5552.
DOI: https://doi.org/10.21276/ijircst.2020.8.4.13   Google Scholar

T. P. P. Padilha, L. E. A. de Lucena, A Systematic Review About Use of TensorFlow for Image Classification and Word Embedding in the Brazilian Context, Academic Journal on Computing, Engineering and Applied Mathematics 1(2) (2020) 24–27.
DOI: https://doi.org/10.20873/uft.2675-3588.2020.v1n2.p24-27   Google Scholar

The CIFAR–10 dataset, https://www.cs.toronto.edu/~kriz/cifar.html [13.02.2021]
  Google Scholar