Deteksi Sel Darah Putih Berdasarkan Citra Mikroskopis Menggunakan Metode Template Matching Berbasis Smartphone
Kata Kunci:
Deteksi sel darah putih, Template matching, Smartphone, Analisis citra, Kecerdasan buatan, Pembelajaran mesin, Computer visionAbstrak
Dalam era kemajuan teknologi medis, analisis citra sel darah putih menjadi sangat penting untuk diagnosis yang lebih cepat dan akurat. Penelitian ini bertujuan untuk menciptakan sistem yang dapat secara otomatis mengidentifikasi dan mengklasifikasikan empat jenis sel darah putih, yaitu neutrofil, limfosit, monosit, dan eosinofil. Dengan memanfaatkan teknologi pembelajaran mesin, sistem ini dirancang untuk meningkatkan efisiensi dan akurasi dalam proses diagnosis, yang sering kali dilakukan secara manual oleh tenaga medis. Metode template matching dipilih karena kemampuannya untuk mencocokkan pola citra dengan akurasi yang baik. Hasil pengujian menunjukkan bahwa sistem ini dapat memproses citra dalam waktu rata-rata 264,67 ms per gambar, dengan akurasi deteksi mencapai 85% menggunakan empat template. Pengujian lebih lanjut mengidentifikasi nilai ambang batas (threshold) optimal sebesar 0,6, yang memberikan keseimbangan terbaik antara akurasi dan efisiensi. Sistem ini juga menunjukkan performa yang baik dalam mengklasifikasikan jenis sel, dengan kesalahan minimal pada pengenalan sel eosinofil dan neutrofil. Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam meningkatkan kualitas layanan kesehatan, terutama di daerah dengan keterbatasan fasilitas medis, serta memperluas aksesibilitas teknologi diagnosis bagi masyarakat.
Referensi
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Almezhghwi, K., & Serte, S. (2020). Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network. Computational Intelligence and Neuroscience, 2020, 1–12. https://doi.org/10.1155/2020/6490479
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Gunawardana, B. R. (2023). Employing Thresholding and Template Rotation to Enhance the Effectiveness of Template Matching Programs. 2023 15th International Conference on Computer and Automation Engineering (ICCAE), 60–64. https://doi.org/10.1109/ICCAE56788.2023.10111421
Han, Y. (2021). Reliable Template Matching for Image Detection in Vision Sensor Systems. Sensors, 21(24), 8176. https://doi.org/10.3390/s21248176
Hosang, J., Benenson, R., & Schiele, B. (2017). Learning Non-maximum Suppression. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6469–6477. https://doi.org/10.1109/CVPR.2017.685
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695. https://doi.org/10.1007/s12525-021-00475-2
Lavitt, F., Rijlaarsdam, D. J., van der Linden, D., Weglarz-Tomczak, E., & Tomczak, J. M. (2021). Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines. Applied Sciences, 11(11), 4912. https://doi.org/10.3390/app11114912
Lee, H., Lee, J.-S., & Choi, H.-C. (2021). Parallelization of Non-Maximum Suppression. IEEE Access, 9, 166579–166587. https://doi.org/10.1109/ACCESS.2021.3134639
Lin, W. N., Tay, M. Z., Lu, R., Liu, Y., Chen, C.-H., & Cheow, L. F. (2020). The Role of Single-Cell Technology in the Study and Control of Infectious Diseases. Cells, 9(6), 1440. https://doi.org/10.3390/cells9061440
Meimban, R. J., Ray Fernando, A., Monsura, A., Ranada, J., & Apduhan, J. C. (2018). Blood Cells Counting using Python OpenCV. 2018 14th IEEE International Conference on Signal Processing (ICSP), 50–53. https://doi.org/10.1109/ICSP.2018.8652384
Navya, K. T., Prasad, K., & Singh, B. M. K. (2021). Classification of blood cells into white blood cells and red blood cells from blood smear images using machine learning techniques. 2021 2nd Global Conference for Advancement in Technology (GCAT), 1–4. https://doi.org/10.1109/GCAT52182.2021.9587524
Pasaribu, T. S. M. (2021). Pengenalan Karakter Huruf Hiragana Menerapkan Metode Template Matching Correlation. Pelita Informatika: Informasi Dan Informatika, 9(4), 303–307.
Wang, Z. J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., Kahng, M., & Polo Chau, D. H. (2021). CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1396–1406. https://doi.org/10.1109/TVCG.2020.3030418
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into Imaging, 9(4), 611–629. https://doi.org/10.1007/s13244-018-0639-9
Zhao, R., & Lu, B. (2023). Flexible template matching for observational study design. Statistics in Medicine, 42(11), 1760–1778. https://doi.org/10.1002/sim.9698
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019). Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865
Alkafrawi, I. M. I., & Dakhell, Z. A. (2022). Blood Cells Classification Using Deep Learning Technique. 2022 International Conference on Engineering & MIS (ICEMIS), 1–6. https://doi.org/10.1109/ICEMIS56295.2022.9914281
Almezhghwi, K., & Serte, S. (2020). Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network. Computational Intelligence and Neuroscience, 2020, 1–12. https://doi.org/10.1155/2020/6490479
Dinas Kesehatan Kota Tegal, (2023). 10 contoh penggunaan teknologi Ai di dunia Kesehatan. [Online] tersedia di: <https://dinkes.tegalkota.go.id/berita/detail/10-contoh-penggunaan-teknologi-ai-di-dunia-kesehatan> [Diakses 27 Agustus 2024]
Gunawardana, B. R. (2023). Employing Thresholding and Template Rotation to Enhance the Effectiveness of Template Matching Programs. 2023 15th International Conference on Computer and Automation Engineering (ICCAE), 60–64. https://doi.org/10.1109/ICCAE56788.2023.10111421
Han, Y. (2021). Reliable Template Matching for Image Detection in Vision Sensor Systems. Sensors, 21(24), 8176. https://doi.org/10.3390/s21248176
Hosang, J., Benenson, R., & Schiele, B. (2017). Learning Non-maximum Suppression. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6469–6477. https://doi.org/10.1109/CVPR.2017.685
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695. https://doi.org/10.1007/s12525-021-00475-2
Lavitt, F., Rijlaarsdam, D. J., van der Linden, D., Weglarz-Tomczak, E., & Tomczak, J. M. (2021). Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines. Applied Sciences, 11(11), 4912. https://doi.org/10.3390/app11114912
Lee, H., Lee, J.-S., & Choi, H.-C. (2021). Parallelization of Non-Maximum Suppression. IEEE Access, 9, 166579–166587. https://doi.org/10.1109/ACCESS.2021.3134639
Lin, W. N., Tay, M. Z., Lu, R., Liu, Y., Chen, C.-H., & Cheow, L. F. (2020). The Role of Single-Cell Technology in the Study and Control of Infectious Diseases. Cells, 9(6), 1440. https://doi.org/10.3390/cells9061440
Meimban, R. J., Ray Fernando, A., Monsura, A., Ranada, J., & Apduhan, J. C. (2018). Blood Cells Counting using Python OpenCV. 2018 14th IEEE International Conference on Signal Processing (ICSP), 50–53. https://doi.org/10.1109/ICSP.2018.8652384
Navya, K. T., Prasad, K., & Singh, B. M. K. (2021). Classification of blood cells into white blood cells and red blood cells from blood smear images using machine learning techniques. 2021 2nd Global Conference for Advancement in Technology (GCAT), 1–4. https://doi.org/10.1109/GCAT52182.2021.9587524
Pasaribu, T. S. M. (2021). Pengenalan Karakter Huruf Hiragana Menerapkan Metode Template Matching Correlation. Pelita Informatika: Informasi Dan Informatika, 9(4), 303–307.
Wang, Z. J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., Kahng, M., & Polo Chau, D. H. (2021). CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1396–1406. https://doi.org/10.1109/TVCG.2020.3030418
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into Imaging, 9(4), 611–629. https://doi.org/10.1007/s13244-018-0639-9
Zhao, R., & Lu, B. (2023). Flexible template matching for observational study design. Statistics in Medicine, 42(11), 1760–1778. https://doi.org/10.1002/sim.9698
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019). Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865
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