Enhancing Image Classification Performance Using Multi CNN Feature Fusion Method

https://doi.org/10.22146/ijccs.98531

Hizbullah Hamda(1), Moh Edi Wibowo(2*)

(1) Universitas Gadjah Mada
(2) (Scopus ID : 54975678100); Department of Computer Science and Electronics, Gadjah Mada University
(*) Corresponding Author

Abstract


This research aims to overcome general challenges in the field of image pattern recognition using a convolutional neural network (CNN), which is still faced with the complexity and limitations of image data. Achieving high accuracy is essential because it significantly influences the effectiveness and success of numerous areas. Although deep learning technology, especially CNNs, offers the potential to improve accuracy, it is still limited to the 70–80% range for achieving the expected level of accuracy. In this research, a fusion method was developed that combines pre-trained models using concatenation techniques to increase accuracy. By utilizing pre-trained models such as ResNet50, VGG16, and MobileNet-v2, which were then adapted to various datasets and cross-validation techniques, researchers managed to achieve significant improvements in accuracy. The results of this study show an improvement in the accuracy of the Fusion Multi-CNN model for various datasets. On the fashion dataset, MNIST managed to achieve an accuracy of 0.87840, while on CIFAR-10 and Oxford-102, the accuracy was 0.81260 and 0.84004, respectively.

Keywords


image classification; enhancement; feature fusion; convolutional neural network

Full Text:

PDF


References

J. Butdee, W. Kongprawechnon, H. Nakahara, N. Chayopitak, C. Kingkan, and R. Pupadubsin, “Pattern Recognition of Partial Discharge Faults Using Convolutional Neural Network (CNN),” in 2023 8th International Conference on Control and Robotics Engineering, Institute of Electrical and Electronics Engineers (IEEE), Jun. 2023, pp. 61–66. doi: 10.1109/iccre57112.2023.10155616. [2] A. Tayal, J. Gupta, A. Solanki, K. Bisht, A. Nayyar, and M. Masud, “Correction to: DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases,” in Multimedia Systems, Springer Science and Business Media Deutschland GmbH, 2021, pp. 1417–1438. doi: 10.1007/s00530-021-00791-9. [3] K. Hidjah, A. Harjoko, M. Edi Wibowo, and R. Ratna Shantiningsih, “Periapical Radiograph Texture Features for Osteoporosis Detection using Deep Convolutional Neural Network,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 1, pp. 223–232, 2022, doi: 10.14569/IJACSA.2022.0130127. [4] D. Kollias and S. Zafeiriou, “Exploiting Multi-CNN Features in CNN-RNN Based Dimensional Emotion Recognition on the OMG in-the-Wild Dataset,” IEEE Trans Affect Comput, vol. 12, no. 3, pp. 595–606, Jul. 2021, doi: 10.1109/TAFFC.2020.3014171. [5] B. Paul and S. Phadikar, “A hybrid feature-extracted deep CNN with reduced parameters substitutes an End-to-End CNN for the recognition of spoken Bengali digits,” Multimed Tools Appl, 2023, doi: 10.1007/s11042-023-15598-1. [6] Z. Yu, J. Tang, and Z. Wang, “GCPS: A CNN Performance Evaluation Criterion for Radar Signal Intrapulse Modulation Recognition,” IEEE Communications Letters, vol. 25, no. 7, pp. 2290– 2294, Jul. 2021, doi: 10.1109/LCOMM.2021.3070151. [7] Wu Zuobin, Mao Kezhi, and Gee -Wah Ng, “Effective feature fusion for pattern classification based on intra-class and extra-class discriminative correlation analysis,” in 20th International Conference on Information Fusion (Fusion), 2017, pp. 1–8. doi: 10.23919/ICIF.2017.8009795. [8] R. Laroca, L. A. Zanlorensi, V. Estevam, R. Minetto, and D. Menotti, “Leveraging Model Fusion for Improved License Plate Recognition,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Springer Science and Business Media Deutschland GmbH, 2024, pp. 60–75. doi: 10.1007/978-3-031-49249-5_5. [9] N. Gawande, D. Goyal, and K. Sankhla, “Improved Deep Learning and Feature Fusion Techniques for Chronic Heart Failure,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 17s, pp. 67–80, 2024, [Online]. Available: www.ijisae.org [10] P. Deepan, “Fusion of Deep Learning Models for Improving Classification Accuracy of Remote Sensing Images,” JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, vol. 14, no. 5, Oct. 2019, doi: 10.26782/jmcms.2019.10.00015. [11] L. Perez and J. Wang, “The Effectiveness of Data Augmentation in Image Classification using Deep Learning,” Dec. 2017, [Online]. Available: http://arxiv.org/abs/1712.04621 [12] N. Srivastava, G. Hinton, A. Krizhevsky, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” 2014. [13] I. Guyon and A. M. De, “An Introduction to Variable and Feature Selection André Elisseeff,” 2003. [14] R. Malhotra and S. Meena, “Empirical Validation of cross-version and 10-fold cross-validation for Defect Prediction,” in Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021, Institute of Electrical and Electronics Engineers Inc., Aug. 2021, pp. 431–438. doi: 10.1109/ICESC51422.2021.9533030. [15] M. Yüzkat, H. O. Ilhan, and N. Aydin, “Multi-Model CNN Fusion for Sperm Morphology Analysis,” Comput Biol Med, vol. 137, pp. 1–12, Oct. 2021, doi: 10.1016/j.compbiomed.2021.104790. [16] Z. A. Sejuti and M. S. Islam, “A hybrid CNN–KNN approach for identification of COVID-19 with 5-fold cross validation,” Sensors International, vol. 4, pp. 1–11, Jan. 2023, doi: 10.1016/j.sintl.2023.100229.



DOI: https://doi.org/10.22146/ijccs.98531

Article Metrics

Abstract views : 3140 | views : 1268

Refbacks

  • There are currently no refbacks.




Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs



View My Stats1
View My Stats2