Klasifikasikan Jenis Cacat Kulit Menggunakan SMOTE-GoogLeNet
Keywords:
classification, data balancing method, deep learning, GoogLeNet, leather defectAbstract
Deep learning has been proven to be able to provide significant contributions to several fields, including industry. It has also been proven that it has resulted in an outstanding performance for classification, detection, and even segmentation processes. In the leather industry, it also successfully gave valuable results, especially for the leather defect inspection process. However, despite its outstanding performance, it remained a drawback because it produced insignificant results if employed in a small or imbalanced dataset. This research work focuses on the analysis of the implementation of the data balancing method for improving the performance of the deep learning method for classifying the types of leather defects. This research work was done by employing three processes. In the first step, we utilized the data balancing method to balance the data proportion. In the next step, we employed GoogLeNet as a deep learning architecture for training and testing processes. Our experiment was conducted in two scenarios. The first scenario was done by using the original dataset. Whereas the second scenario was accomplished by utilizing the data balancing method before training and testing. According to the experiment results, implementing the data balancing method successfully increased the performance of the deep learning method by more than 15%. It can be inferred that the proportion or the number of data strongly affected the performance of deep learning models.
References
S. Liong, Y. Gan, Y.-C. Huang, C.-A. Yuan, and H.-C. Chang, “Automatic Defect Segmentation on Leather with Deep Learning,” arXiv, Mar. 2019, doi: https://doi.org/10.48550/arXiv.1903.12139.
S. T. Liong, D. Zheng, Y. C. Huang, and Y. S. Gan, “Leather defect classification and segmentation using deep learning architecture,” Int. J. Comput. Integr. Manuf., vol. 33, no. 10–11, pp. 1105–1117, 2020, doi: 10.1080/0951192X.2020.1795928.
S. N. Vasagam and M. Sornam, “Intermittent Leather Defect Detection Based on Ensemble Algorithms Derived from Black Hat Transformation and Hough Transformation BT - ICT Analysis and Applications,” 2022, pp. 35–45.
Vasagam, S. N. Vasagam, and M. Sornam, “Species Wise Classification of Crust Leather images based on histogram equalization,” in 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022, pp. 1307–1312. doi: 10.1109/ICCMC53470.2022.9753704.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818–2826. doi: 10.1109/CVPR.2016.308.
L. Alzubaidi et al., “Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study,” Applied Sciences , vol. 10, no. 13. 2020. doi: 10.3390/app10134523.
L. Alzubaidi et al., “Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data,” Cancers (Basel)., vol. 13, no. 7, p. 1590, Mar. 2021, doi: 10.3390/cancers13071590.
M. Jawahar, N. K. C. Babu, and K. Vani, “Leather texture classification using wavelet feature extraction technique,” in 2014 IEEE International Conference on Computational Intelligence and Computing Research, 2014, pp. 1–4. doi: 10.1109/ICCIC.2014.7238475.
H.-Q. Bong, Q.-B. Truong, H.-C. Nguyen, and M.-T. Nguyen, “Vision-based Inspection System for Leather Surface Defect Detection and Classification,” in 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), 2018, pp. 300–304. doi: 10.1109/NICS.2018.8606836.
S.-T. Liong, Y. S. Gan, Y.-C. Huang, K.-H. Liu, and W.-C. Yau, “Integrated Neural Network and Machine Vision Approach For Leather Defect Classification,” 2019, [Online]. Available: http://arxiv.org/abs/1905.11731
C. Szegedy et al., “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–9. doi: 10.1109/CVPR.2015.7298594.
A. Ahmad et al., “Vehicle Recognition using Multi-Layer Perceptron and SMOTE Technique,” in 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH), 2022, pp. 190–193. doi: 10.1109/SMARTTECH54121.2022.00049.
O. P. Barus, J. Happy, Jusin, J. J. Pangaribuan, S. Z. H, and F. Nadjar, “Liver Disease Prediction Using Support Vector Machine and Logistic Regression Model with Combination of PCA and SMOTE,” in 2022 1st International Conference on Technology Innovation and Its Applications (ICTIIA), 2022, pp. 1–6. doi: 10.1109/ICTIIA54654.2022.9935879.
E. Chamseddine, N. Mansouri, M. Soui, and M. Abed, “Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss,” Appl. Soft Comput., vol. 129, p. 109588, 2022, doi: https://doi.org/10.1016/j.asoc.2022.109588.
A. Özdemir, K. Polat, and A. Alhudhaif, “Classification of imbalanced hyperspectral images using SMOTE-based deep learning methods,” Expert Syst. Appl., vol. 178, p. 114986, 2021, doi: https://doi.org/10.1016/j.eswa.2021.114986.
B. Ibrahim, I. Ahenkorah, A. Ewusi, and F. Majeed, “A novel XRF-based lithological classification in the Tarkwaian paleo placer formation using SMOTE-XGBoost,” J. Geochemical Explor., vol. 245, p. 107147, 2023, doi: https://doi.org/10.1016/j.gexplo.2022.107147.
S. Liong, Y. Gan, Y.-C. Huang, C.-A. Yuan, and H.-C. Chang, “Automatic Defect Segmentation on Leather with Deep Learning,” arXiv, Mar. 2019, doi: https://doi.org/10.48550/arXiv.1903.12139.
S. T. Liong, D. Zheng, Y. C. Huang, and Y. S. Gan, “Leather defect classification and segmentation using deep learning architecture,” Int. J. Comput. Integr. Manuf., vol. 33, no. 10–11, pp. 1105–1117, 2020, doi: 10.1080/0951192X.2020.1795928.
T. Spahiu, E. Canaj, and E. Shehi, “3D printing for clothing production,” J. Eng. Fiber. Fabr., vol. 15, pp. 1–8, 2020, doi: 10.1177/1558925020948216.
S. N. Vasagam and M. Sornam, “Intermittent Leather Defect Detection Based on Ensemble Algorithms Derived from Black Hat Transformation and Hough Transformation BT - ICT Analysis and Applications,” 2022, pp. 35–45.
Vasagam, S. N. Vasagam, and M. Sornam, “Species Wise Classification of Crust Leather images based on histogram equalization,” in 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022, pp. 1307–1312. doi: 10.1109/ICCMC53470.2022.9753704.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818–2826. doi: 10.1109/CVPR.2016.308.
L. Alzubaidi et al., “Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study,” Applied Sciences , vol. 10, no. 13. 2020. doi: 10.3390/app10134523.
L. Alzubaidi et al., “Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data,” Cancers (Basel)., vol. 13, no. 7, p. 1590, Mar. 2021, doi: 10.3390/cancers13071590.
M. Jawahar, N. K. C. Babu, and K. Vani, “Leather texture classification using wavelet feature extraction technique,” in 2014 IEEE International Conference on Computational Intelligence and Computing Research, 2014, pp. 1–4. doi: 10.1109/ICCIC.2014.7238475.
H.-Q. Bong, Q.-B. Truong, H.-C. Nguyen, and M.-T. Nguyen, “Vision-based Inspection System for Leather Surface Defect Detection and Classification,” in 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), 2018, pp. 300–304. doi: 10.1109/NICS.2018.8606836.
S.-T. Liong, Y. S. Gan, Y.-C. Huang, K.-H. Liu, and W.-C. Yau, “Integrated Neural Network and Machine Vision Approach For Leather Defect Classification,” 2019, [Online]. Available: http://arxiv.org/abs/1905.11731
A. Ahmad et al., “Vehicle Recognition using Multi-Layer Perceptron and SMOTE Technique,” in 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH), 2022, pp. 190–193. doi: 10.1109/SMARTTECH54121.2022.00049.
O. P. Barus, J. Happy, Jusin, J. J. Pangaribuan, S. Z. H, and F. Nadjar, “Liver Disease Prediction Using Support Vector Machine and Logistic Regression Model with Combination of PCA and SMOTE,” in 2022 1st International Conference on Technology Innovation and Its Applications (ICTIIA), 2022, pp. 1–6. doi: 10.1109/ICTIIA54654.2022.9935879.
E. Chamseddine, N. Mansouri, M. Soui, and M. Abed, “Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss,” Appl. Soft Comput., vol. 129, p. 109588, 2022, doi: https://doi.org/10.1016/j.asoc.2022.109588.
A. Özdemir, K. Polat, and A. Alhudhaif, “Classification of imbalanced hyperspectral images using SMOTE-based deep learning methods,” Expert Syst. Appl., vol. 178, p. 114986, 2021, doi: https://doi.org/10.1016/j.eswa.2021.114986.
B. Ibrahim, I. Ahenkorah, A. Ewusi, and F. Majeed, “A novel XRF-based lithological classification in the Tarkwaian paleo placer formation using SMOTE-XGBoost,” J. Geochemical Explor., vol. 245, p. 107147, 2023, doi: https://doi.org/10.1016/j.gexplo.2022.107147.
A. O. Widodo, B. Setiawan, and R. Indraswari, “Machine Learning-Based Intrusion Detection on Multi-Class Imbalanced Dataset Using SMOTE,” Procedia Comput. Sci., vol. 234, pp. 578–583, 2024, doi: https://doi.org/10.1016/j.procs.2024.03.042.
Q. Zeng, Z. Gong, S. Wu, C. Zhuang, and S. Li, “Measuring cyclists’ subjective perceptions of the street riding environment using K-means SMOTE-RF model and street view imagery,” Int. J. Appl. Earth Obs. Geoinf., vol. 128, p. 103739, 2024, doi: https://doi.org/10.1016/j.jag.2024.103739.
C. Szegedy et al., “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–9. doi: 10.1109/CVPR.2015.7298594.
1.png)
