Enhancing Image Classification Performance Using Multi CNN Feature Fusion Method

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
Keywords
Full Text:
PDFReferences
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.

Article Metrics


Refbacks
- There are currently no refbacks.
Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1