BI-DIRECTIONAL CLASSIFICATION OF ROMAN PERIOD COINS BY DEEP LEARNING METHODS

Abstract

In this study, the problem of classification of coins, which have historical importance and can only be distinguished by experts, is discussed with pre-learning deep learning algorithms. In the solution of the problem, the RRC-60 dataset, which consists of the images of the coins used in the Roman Republic period, was used. In this study, pre-learning Xception, MobileNetV3-L, EfficientNetB0 and DenseNet201 models were trained using the images on both sides of the coins in the data set. As a result of the training, the best values, Precision, Recall and F1-Score metrics in the MobileNetV3-L model were 98.2%, 96.8%, 97.5%, respectively, and the test accuracy was 95.2%

Keywords

• classification
• CNN
• coin
• deep learning

References

1. [1] A. I. Khan, S. M. K. Quadri, S. Banday, and J. L. Shah, “Deep diagnosis: A real-time apple leaf disease detection system based on deep learning,” Comput. Electron. Agric., vol. 198, p. 107093, 2022.
2. [2] F. Şenel, “A Hyperparameter Optimization for Galaxy Classification,” Comput. Mater. Contin., vol. 74, no. 2, 2023.
3. [3] H. Temiz, B. Gökberk, and L. Akarun, “TurCoins: Turkish republic coin dataset,” in 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1–4.
4. [4] S. Aslan, S. Vascon, and M. Pelillo, “Two sides of the same coin: Improved ancient coin classification using Graph Transduction Games,” Pattern Recognit. Lett., vol. 131, pp. 158–165, 2020. [5] A. G. YILMAZ, Ç. SAYGILI, and V. NABİYEV, “Roma Dönemi Madeni Paralarının Derin Öğrenme Tabanlı Sınıflandırılması.”
5. [6] H. Anwar, S. Sabetghadam, and P. Bell, “An Image-Based Class Retrieval System for Roman Republican Coins,” Entropy, vol. 22, no. 8, p. 799, 2020.
6. [7] Y. Ma and O. Arandjelović, “Classification of ancient roman coins by denomination using colour, a forgotten feature in automatic ancient coin analysis,” Sci, vol. 2, no. 2, p. 37, 2020.
7. [8] H. Anwar, S. Anwar, S. Zambanini, and F. Porikli, “Deep ancient Roman Republican coin classification via feature fusion and attention,” Pattern Recognit., vol. 114, p. 107871, 2021.
8. [9] G. Ciaburro and B. Venkateswaran, Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing Ltd, 2017.

9. [10] M. Crawford, “Coinage of the Roman Republic Online,” 2021. http://numismatics.org/crro/.
10. [11] M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning, 2019, pp. 6105–6114.
11. [12] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700–4708.
12. [13] A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, and M. Kaur, “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning,” J. Biomol. Struct. Dyn., vol. 39, no. 15, pp. 5682–5689, 2021.
13. [14] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251–1258.
14. A. Howard et al., “Searching for mobilenetv3,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 1314–1324.