Pengembangan Sistem Deteksi Jenis Sel Darah Putih Menggunakan Algoritma Shape Context Berbasis Smartphone
Kata Kunci:
Teknologi, Sel Darah Putih, Shape Context, Pembelajaran Mesin, Kecerdasan Buatan, SmartphoneAbstrak
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 kecerdasan buatan dan pembelajaran mesin, sistem ini dirancang untuk meningkatkan efisiensi dan akurasi dalam proses diagnosis, yang sering kali dilakukan secara manual oleh tenaga medis. Metode Shape Context dipilih karena kemampuannya dalam menganalisis kontur dan pola objek dengan presisi tinggi, memungkinkan sistem mengenali perbedaan fitur kompleks dari masing-masing jenis sel darah putih. Hasil pengujian menunjukkan bahwa sistem ini dapat memproses gambar dalam waktu rata-rata 113.20 ms per gambar, yang menunjukkan efisiensi yang baik untuk aplikasi berbasis smartphone. Selain itu, sistem ini mampu mengakses file gambar dengan ekstensi JPEG dan PNG, serta melakukan deteksi sel pada citra mikroskopik. Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam meningkatkan kualitas layanan kesehatan, terutama di daerah dengan keterbatasan fasilitas medis, serta memperluas aksesibilitas teknologi diagnostik bagi masyarakat.
Referensi
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Garg, S., & Baliyan, N. (2021). Comparative analysis of Android and iOS from security viewpoint. Computer Science Review, 40, 100372. https://doi.org/10.1016/j.cosrev.2021.100372
Halim, W., & Mudjihartono, P. (2022). Kecerdasan Buatan dalam Teknologi Kedokteran: Survey Paper. KONSTELASI: Konvergensi Teknologi Dan Sistem Informasi, 2(1), 207–216.
Heni, A., Jdey, I., & Ltifi, H. (2023). Blood Cells Classification Using Deep Learning With Customized Data Augmentation and Ek-Means Segmentation. Journal of Theoretical and Applied Information Technology, 101(3), 1162–1173.
Humphry, E., & Armstrong, C. E. (2022). Physiology of red and white blood cells. Anaesthesia and Intensive Care Medicine, 23(2), 118–122. https://doi.org/10.1016/j.mpaic.2021.10.019
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Liyantoko, A. N., Candradewi, I., & Harjoko, A. (2019). Classification of White Blood Cells and Lymphoblast Cells Using Multilayer Perceptron Backpropagation Method. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 9(2), 173.
Martomanggolo, D. (2021). Perbandingan Convolutional Neural Network pada Transfer Learning Method untuk Mengklasifikasikan Sel Darah Putih. Ultimatics : Jurnal Teknik Informatika, 13(1), 51.
Mori, G., Belongie, S., & Malik, J. (2005). Efficient shape matching using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11), 1832–1837. https://doi.org/10.1109/TPAMI.2005.220
Rossa, M. A., & Nuryadi, S. (2019). Identifikasi Kelainan Sel Darah Merah Menggunakan Teknik Pengolahan Citra Digital. Naskah Publikasi Teknik Elektro.
Saksenata, A. F., Minarno, A. E., & Azhar, Y. (2022). Klasifikasi Citra Sel Darah Untuk Penyakit Malaria Dengan Metode CNN. Jurnal Repositor, 4(2), 185–194. https://doi.org/10.22219/repositor.v4i2.1283
Salve, S. G., & Jondhale, K. C. (2010). Shape matching and object recognition using shape contexts. Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010, 9, 471–474. https://doi.org/10.1109/ICCSIT.2010.5565098
Tamang, T., Baral, S., & Paing, M. P. (2022). Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks. Diagnostics, 12(12). https://doi.org/10.3390/diagnostics12122903
Tian, Z., Wei, Y., Yu, Y., Zhou, F., & Huang, Z. L. (2022). Blood Cell Analysis: From Traditional Methods to Super-Resolution Microscopy. Photonics, 9(4). https://doi.org/10.3390/photonics9040261
Venkata Mahesh Babu Batta. (2024). Machine Learning. International Journal of Advanced Research in Science, Communication and Technology, 583–591. https://doi.org/10.48175/ijarsct-17677
Antoni, M. S., & Suharjana, S. (2019). Aplikasi kebugaran dan kesehatan berbasis android: Bagaimana persepsi dan minat masyarakat? Jurnal Keolahragaan, 7(1), 34–42. https://doi.org/10.21831/jk.v7i1.21571
Badillo, S., Banfai, B., Birzele, F., Davydov, I. I., Hutchinson, L., Kam-Thong, T., Siebourg-Polster, J., Steiert, B., & Zhang, J. D. (2020). An Introduction to Machine Learning. Clinical Pharmacology and Therapeutics, 107(4), 871–885. https://doi.org/10.1002/cpt.1796
Belongie, S., & Malik, J. (2000). Matching with shape contexts. Proceedings - IEEE Workshop on Content-Based Access of Image and Video Libraries, CBAIVL 2000, c, 20–26. https://doi.org/10.1109/IVL.2000.853834
Garg, S., & Baliyan, N. (2021). Comparative analysis of Android and iOS from security viewpoint. Computer Science Review, 40, 100372. https://doi.org/10.1016/j.cosrev.2021.100372
Halim, W., & Mudjihartono, P. (2022). Kecerdasan Buatan dalam Teknologi Kedokteran: Survey Paper. KONSTELASI: Konvergensi Teknologi Dan Sistem Informasi, 2(1), 207–216.
Heni, A., Jdey, I., & Ltifi, H. (2023). Blood Cells Classification Using Deep Learning With Customized Data Augmentation and Ek-Means Segmentation. Journal of Theoretical and Applied Information Technology, 101(3), 1162–1173.
Humphry, E., & Armstrong, C. E. (2022). Physiology of red and white blood cells. Anaesthesia and Intensive Care Medicine, 23(2), 118–122. https://doi.org/10.1016/j.mpaic.2021.10.019
Journal, I. (2022). Smartphone: IOS Vs Android. Interantional Journal of Scientific Research in Engineering and Management, 06(06), 1–7. https://doi.org/10.55041/ijsrem14475
Lee, Y. (2023). The CNN: The Architecture Behind Artificial Intelligence Development. Journal of Student Research, 12(4), 1–12. https://doi.org/10.47611/jsrhs.v12i4.5579
Li, Y. F., Hou, Z. Y., Yang, C. K., & Lai, Y. C. (2024). Path Finding via Shape Context Matching. 2024 16th International Conference on Advanced Computational Intelligence, ICACI 2024, Icaci, 55–64. https://doi.org/10.1109/ICACI60820.2024.10537007
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999–7019. https://doi.org/10.1109/TNNLS.2021.3084827
Liyantoko, A. N., Candradewi, I., & Harjoko, A. (2019). Classification of White Blood Cells and Lymphoblast Cells Using Multilayer Perceptron Backpropagation Method. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 9(2), 173.
Martomanggolo, D. (2021). Perbandingan Convolutional Neural Network pada Transfer Learning Method untuk Mengklasifikasikan Sel Darah Putih. Ultimatics : Jurnal Teknik Informatika, 13(1), 51.
Mori, G., Belongie, S., & Malik, J. (2005). Efficient shape matching using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11), 1832–1837. https://doi.org/10.1109/TPAMI.2005.220
Rossa, M. A., & Nuryadi, S. (2019). Identifikasi Kelainan Sel Darah Merah Menggunakan Teknik Pengolahan Citra Digital. Naskah Publikasi Teknik Elektro.
Saksenata, A. F., Minarno, A. E., & Azhar, Y. (2022). Klasifikasi Citra Sel Darah Untuk Penyakit Malaria Dengan Metode CNN. Jurnal Repositor, 4(2), 185–194. https://doi.org/10.22219/repositor.v4i2.1283
Salve, S. G., & Jondhale, K. C. (2010). Shape matching and object recognition using shape contexts. Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010, 9, 471–474. https://doi.org/10.1109/ICCSIT.2010.5565098
Tamang, T., Baral, S., & Paing, M. P. (2022). Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks. Diagnostics, 12(12). https://doi.org/10.3390/diagnostics12122903
Tian, Z., Wei, Y., Yu, Y., Zhou, F., & Huang, Z. L. (2022). Blood Cell Analysis: From Traditional Methods to Super-Resolution Microscopy. Photonics, 9(4). https://doi.org/10.3390/photonics9040261
Venkata Mahesh Babu Batta. (2024). Machine Learning. International Journal of Advanced Research in Science, Communication and Technology, 583–591. https://doi.org/10.48175/ijarsct-17677
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