Klasifikasi Citra Limbah Medis Menggunakan Deep Learning

• Sri Nuarini
• 14220016

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ABSTRAK

Kebutuhan akan pengelolaan limbah medis semakin meningkat seiring dengan peningkatan produksi limbah. Saat ini produksi limbah medis melampaui kapasitas pengelolaan. Terutama untuk pemilahan limbah medis. Ke depannya sangat dibutuhkan pengelolaan berbasis digital yang memungkinkan proses klasifikasi limbah medis dilakukan secara otomatis. Sehingga pengelolaan sampah medis lebih cepat. Tujuan dari penelitian ini dibuat adalah untuk mengetahui seberapa besar kinerja data augmentasi pada transfer learning dapat mengklasifikasi limbah medis dengan baik. Penelitian ini mengusulkan pendekatan pembelajaran mendalam untuk identifikasi dan klasifikasi limbah medis. Teknik pembelajaran mendalam digunakan untuk klasifikasi citra limbah medis. Dataset diambil dari rumah sakit di Indonesia dengan 9 kelas jenis limbah medis yang memiliki jenis limbah lebih banyak dibandingkan dataset sejenis. Dalam hal ini penulis menemukan jenis limbah di ruang operasi. Dalam skenario ini, penulis membandingkan model klasifikasi berbasis pembelajaran mendalam, yaitu CNN dan VGG16 dengan metode transfer learning untuk meningkatkan hasil klasifikasi. Hasil perbandingan menunjukkan VGG16 dengan transfer learning memberikan akurasi klasifikasi lebih baik sebesar 98 % lebih tinggi dibandingkan dengan CNN. Kebutuhan akan pengelolaan limbah medis semakin meningkat seiring dengan peningkatan produksi limbah. Saat ini produksi limbah medis melampaui kapasitas pengelolaan. Terutama untuk pemilahan limbah medis. Ke depannya sangat dibutuhkan pengelolaan berbasis digital yang memungkinkan proses klasifikasi limbah medis dilakukan secara otomatis. Sehingga pengelolaan sampah medis lebih cepat. Tujuan dari penelitian ini dibuat adalah untuk mengetahui seberapa besar kinerja data augmentasi pada transfer learning dapat mengklasifikasi limbah medis dengan baik. Penelitian ini mengusulkan pendekatan pembelajaran mendalam untuk identifikasi dan klasifikasi limbah medis. Teknik pembelajaran mendalam digunakan untuk klasifikasi citra limbah medis. Dataset diambil dari rumah sakit di Indonesia dengan 9 kelas jenis limbah medis yang memiliki jenis limbah lebih banyak dibandingkan dataset sejenis. Dalam hal ini penulis menemukan jenis limbah di ruang operasi. Dalam skenario ini, penulis membandingkan model klasifikasi berbasis pembelajaran mendalam, yaitu CNN dan VGG16 dengan metode transfer learning untuk meningkatkan hasil klasifikasi. Hasil perbandingan menunjukkan VGG16 dengan transfer learning memberikan akurasi klasifikasi lebih baik sebesar 98 % lebih tinggi dibandingkan dengan CNN.

Kata kunci : Limbah Medis, CNN, Deep Learning, VGG16

KATA KUNCI

Limbah Medis,Deep Learning

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DAFTAR PUSTAKA

[1] B. Erdebilli and B. Devrim-?çtenba?, “Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey,” Mathematics, vol. 10, no. 14, Jul. 2022, doi: 10.3390/math10142466.

[2] H. Zhou et al., “A deep learning approach for medical waste classification,” Sci Rep, vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-06146-2.

[3] 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society : “Enabling Innovative Technologies for Global Healthcare” : 20-24 July 2020, Montreal, Canada. IEEE, 2020.

[4] V. K. Nerkar and N. N. Mandaogade, “Computer Vision Based Automatic Medical Waste Classification Using Machine Learning,” International Journal of Advances in Engineering and Management (IJAEM), vol. 5, p. 1297, 2023, doi: 10.35629/5252-050512971313.

[5] H. nan Guo, S. biao Wu, Y. jie Tian, J. Zhang, and H. tao Liu, “Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review,” Jan. 01, 2021, Elsevier Ltd. doi: 10.1016/j.biortech.2020.124114.

[6] W. Hurst et al., “Solid Waste Image Classification Using Deep Convolutional Neural Network,” 2022, doi: 10.3390/infrastructures.

[7] V. Ruiz, Á. Sánchez, J. F. Vélez, and B. Raducanu, “Automatic Image-Based Waste Classification,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2019, pp. 422–431. doi: 10.1007/978-3-030-19651-6_41.

[8] C. Wang, J. Qin, C. Qu, X. Ran, C. Liu, and B. Chen, “A smart municipal waste management system based on deep-learning and Internet of Things,” Waste Management, vol. 135, pp. 20–29, Nov. 2021, doi: 10.1016/j.wasman.2021.08.028.

[9] K. Nasik Sami, Z. Md Afique Amin, and R. Hassan, “Waste Management Using Machine Learning and Deep Learning Algorithms,” 2020.

[10] B. Marami and A. R. Royaee, “Automatic Detection and Classification of Waste Consumer Medications for Proper Management and Disposal.”

[11] L. Wang and K. Zuo, “Medical Data Classification Assisted by Machine Learning Strategy,” Comput Math Methods Med, vol. 2022, 2022, doi: 10.1155/2022/9699612.

[12] M. Kaya, S. Ulutürk, Y. Ç. Kaya, O. Alt?nta?, and B. Turan, “Optimization of Several Deep CNN Models for Waste Classification,” Sakarya University Journal of Computer and Information Sciences, vol. 6, no. 2, p. 2023, doi: 10.35377/saucis…1257100.

[13] M. Malik et al., “Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models,” Sustainability (Switzerland), vol. 14, no. 12, Jun. 2022, doi: 10.3390/su14127222.

[14] P. Wang, E. Fan, and P. Wang, “Comparative analysis of image classification algorithms based on traditional machine learning and deep learning,” Pattern Recognit Lett, vol. 141, pp. 61–67, Jan. 2021, doi: 10.1016/j.patrec.2020.07.042.

[15] L. Chen, S. Li, Q. Bai, J. Yang, S. Jiang, and Y. Miao, “Review of image classification algorithms based on convolutional neural networks,” Nov. 01, 2021, MDPI. doi: 10.3390/rs13224712.

[16] Y. Liu, P. Sun, N. Wergeles, and Y. Shang, “A survey and performance evaluation of deep learning methods for small object detection,” Expert Syst Appl, vol. 172, Jun. 2021, doi: 10.1016/j.eswa.2021.114602.

[17] R. Yang and Y. Yu, “Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis,” Mar. 09, 2021, Frontiers Media S.A. doi: 10.3389/fonc.2021.638182.

[18] S. Hao, Y. Zhou, and Y. Guo, “A Brief Survey on Semantic Segmentation with Deep Learning,” Neurocomputing, vol. 406, pp. 302–321, Sep. 2020, doi: 10.1016/j.neucom.2019.11.118.

[19] K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.

[20] C. Bu and Z. Zhang, “Research on Overfitting Problem and Correction in Machine Learning,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Dec. 2020. doi: 10.1088/1742- 6596/1693/1/012100.

[21] S. Hadianti, F. Aziz, D. Nur Sulistyowati, D. Riana, R. Saputra, and Kurniawantoro, “Identification of Potato Plant Pests Using the Convolutional Neural Network VGG16 Method,” Journal Medical Informatics Technology, pp. 39–44, Jun. 2024, doi: 10.37034/medinftech.v2i2.37.

[22] W. L. Mao, W. C. Chen, C. T. Wang, and Y. H. Lin, “Recycling waste classification using optimized convolutional neural network,” Resour Conserv Recycl, vol. 164, Jan. 2021, doi: 10.1016/j.resconrec.2020.105132.

[23] A. Yang, X. Yang, W. Wu, H. Liu, and Y. Zhuansun, “Research on feature extraction of tumor image based on convolutional neural network,” IEEE Access, vol. 7, pp. 24204–24213, 2019, doi: 10.1109/ACCESS.2019.2897131.

[24] S. H. S. Basha, S. R. Dubey, V. Pulabaigari, and S. Mukherjee, “Impact of fully connected layers on performance of convolutional neural networks for image classification,” Neurocomputing, vol. 378, pp. 112–119, Feb. 2020, doi: 10.1016/j.neucom.2019.10.008.

[25] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects,” IEEE Trans Neural Netw Learn Syst, vol. 33, no. 12, pp. 6999–7019, Dec. 2022, doi: 10.1109/TNNLS.2021.3084827.

[26] W. Hao, W. Yizhou, L. Yaqin, and S. Zhili, “The Role of Activation Function in CNN,” in Proceedings - 2020 2nd International Conference on Information Technology and Computer Application, ITCA 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 429– 432. doi: 10.1109/ITCA52113.2020.00096.

[27] 10th International Conference on Cloud Computing, Data Science & Engineering : proceedings of the Confluence 2020 : 29-31 January 2020, Amity University, Uttar Pradesh, India. IEEE, 2020.

[28] M. M. Taye, “Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions,” Mar. 01, 2023, MDPI. doi: 10.3390/computation11030052.

[29] R. Riad, O. Teboul, D. Grangier, and N. Zeghidour, “Learning strides in convolutional neural networks,” Feb. 2022, [Online]. Available: http://arxiv.org/abs/2202.01653

[30] T. Haryanto, I. S. Sitanggang, M. A. Agmalaro, and R. Rulaningtyas, “The Utilization of Padding Scheme on Convolutional Neural Network for Cervical Cell Images Classification,” in CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020, pp. 34–38. doi: 10.1109/CENIM51130.2020.9297895.

[31] M. Dwarampudi and N. V. S. Reddy, “Effects of padding on LSTMs and CNNs,” Mar. 2019, [Online]. Available: http://arxiv.org/abs/1903.07288

[32] H. Gholamalinezhad and H. Khosravi, “Pooling Methods in Deep Neural Networks, a Review.” [33] F. Nurona Cahya et al., “SISTEMASI: Jurnal Sistem Informasi Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network ( CNN).” [Online]. Available: http://sistemasi.ftik.unisi.ac.id [

34] I. Kandel and M. Castelli, “Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review,” Mar. 01, 2020, MDPI AG. doi: 10.3390/app10062021.

[35] N. Abou Baker, N. Zengeler, and U. Handmann, “A Transfer Learning Evaluation of Deep Neural Networks for Image Classification,” Mach Learn Knowl Extr, vol. 4, no. 1, pp. 22–41, Mar. 2022, doi: 10.3390/make4010002.

[36] A. A. Alatawi, S. M. Alomani, N. I. Alhawiti, and M. Ayaz, “Plant Disease Detection using AI based VGG-16 Model.” [Online]. Available: www.ijacsa.thesai.org

[37] V. Patel, M. Kanojia, and V. Nair, “Exploring the Potential of ResNet50 and YOLOv8 in Improving Breast Cancer Diagnosis: A Deep Learning Perspective,” 2024.

[38] M. Shafiq and Z. Gu, “Deep Residual Learning for Image Recognition: A Survey,” Sep. 01, 2022, MDPI. doi: 10.3390/app12188972.

[39] D. Krstini?, M. Braovi?, L. Šeri?, and D. Boži?-Štuli?, “Multi-label Classifier Performance Evaluation with Confusion Matrix,” Academy and Industry Research Collaboration Center (AIRCC), Jun. 2020, pp. 01–14. doi: 10.5121/csit.2020.100801.

[40] A. Ahuja, L. Al-Zogbi, and A. Krieger, “Application of noise-reduction techniques to machine learning algorithms for breast cancer tumor identification,” Comput Biol Med, vol. 135, Aug. 2021, doi: 10.1016/j.compbiomed.2021.104576.

[41] 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) : March 26-28, 2021, Nanchang, China. Institute of Electrical and Electronics Engineers, Inc., 2021.

[42] P. Akkajit and A. Sukkuea, “Medical Waste Classification Using Convolutional Neural Network,” in E3S Web of Conferences, EDP Sciences, May 2024. doi: 10.1051/e3sconf/202453004001.

[43] O. Adedeji and Z. Wang, “Intelligent waste classification system using deep learning convolutional neural network,” in Procedia Manufacturing, Elsevier B.V., 2019, pp. 607–612. doi: 10.1016/j.promfg.2019.05.086.

[44] D. O. Melinte, A. M. Travediu, and D. N. Dumitriu, “Deep convolutional neural networks object detector for real-time waste identification,” Applied Sciences (Switzerland), vol. 10, no. 20, pp. 1– 18, Oct. 2020, doi: 10.3390/app10207301.

[45] G. L. Huang, J. He, Z. Xu, and G. Huang, “A combination model based on transfer learning for waste classification,” Concurr Comput, vol. 32, no. 19, Oct. 2020, doi: 10.1002/cpe.5751.

[46] N. M. Kumar et al., “Artificial intelligence-based solution for sorting COVID related medical waste streams and supporting data-driven decisions for smart circular economy practice,” Process Safety and Environmental Protection, vol. 152, pp. 482–494, Aug. 2021, doi: 10.1016/j.psep.2021.06.026.

[47] Q. Zhang, Q. Yang, X. Zhang, Q. Bao, J. Su, and X. Liu, “Waste image classification based on transfer learning and convolutional neural network,” Waste Management, vol. 135, pp. 150–157, Nov. 2021, doi: 10.1016/j.wasman.2021.08.038.