Model Hybrid CNN Mengintegrasikan NasNetMobile dan MobileNet untuk Meningkatkan Akurasi Klasifikasi White Blood Cell
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Sandi Putra Siregar
*
STIKOM Tunas Bangsa, Pematang Siantar,
Indonesia
- Anjar Wanto STIKOM Tunas Bangsa, Pematang Siantar, Indonesia
- Sundari Retno Andani STIKOM Tunas Bangsa, Pematang Siantar, Indonesia
Abstract
Sel darah putih merupakan komponen vital dalam sistem kekebalan tubuh pada manusia yang berperan penting dalam melindungi tubuh dari serangan mikroorganisme penyebab penyakit. Variabilitas hasil dalam klasifikasi sel darah putih yang disebabkan oleh keterbatasan metode identifikasi manual masih menjadi isu kritis bagi akurasi system diagnostic berbasis citra. Dalam studi ini difokuskan untuk mengatasi permasalahan tersebut dengan merancang model jarignan saraf konvolusional (CNN) hybrid baru yang dinamakan SAN-Net, yang mengintegrasikan keunggulan arsitektur NASNetMobile dan MobileNet guna meningkatkan akurasi dalam klasifikasi jenis sel darah putih (basophil, erythroblast, monocyte, myeloblast, dan seg neutrophil). Model yang diusulkan dilatih menggunakan dataset citra sel darah putih yang dikumpulkan dari Kaggle kemudian dibandingakan dengan arsitektur standar yakni NASNetMobile. Hasil Pengujian menunjukkan bahwa model SAN-Net memberikan performa terbaik, dengan capaian akurasi, presisi, recall, dan Skor F1 sebesar 99,80%, serta secara signifikasi melampaui kinerja model pembanding. Temuan ini mengindikasikan bahwa potensi arsitektur deep learning modern dalam menghadirkan sistem klasifikasi sel darah putih otomatis dengan konsisten dan akurat, sehingga dapat meningkatkan efisiensi proses diagnosis.
References
Abdullah, M. A. S. A. G. (2022). Face Mask Detection During COVID-19 Pandemic using NASNetMobile and CNN Deep Learning Algorithms. 4th International Conference on Current Research in Engineering and Science Applications Iccresa 2022, 137–142. https://doi.org/10.1109/ICCRESA57091.2022.10352501
Ahsan, M. M. (2020). COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities. Machine Learning and Knowledge Extraction, 2(4), 490–504. https://doi.org/10.3390/make2040027
Almurayziq, T. S. (2023). Deep and Hybrid Learning Techniques for Diagnosing Microscopic Blood Samples for Early Detection of White Blood Cell Diseases. Electronics Switzerland, 12(8). https://doi.org/10.3390/electronics12081853
Altomi, Z. A. (2025). Autism Spectrum Disorder Diagnosis Based on Attentional Feature Fusion Using NasNetMobile and DeiT Networks. Electronics Switzerland, 14(9). https://doi.org/10.3390/electronics14091822
Amalia, L. (2020). Clinical significance of Platelet-to-White Blood Cell Ratio (PWR) and National Institute of Health Stroke Scale (NIHSS) in acute ischemic stroke. Heliyon, 6(10). https://doi.org/10.1016/j.heliyon.2020.e05033
Barroso, I. (2023). White blood cells in a healthy adolescent population according to social and health characteristics. Archives De Pediatrie, 30(6), 361–365. https://doi.org/10.1016/j.arcped.2023.03.008
Beydoun, H. A. (2020). Periodontal disease, sleep duration, and white blood cell markers in the 2009 to 2014 National Health and Nutrition Examination Surveys. Journal of Periodontology, 91(5), 582–595. https://doi.org/10.1002/JPER.19-0055
Çınar, A. (2021). Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM. SN Applied Sciences, 3(4). https://doi.org/10.1007/s42452-021-04485-9
Dishar, H. K. (2023). Detection Brain Tumor Disease Using a Combination of Xception and NASNetMobile. International Journal of Advances in Soft Computing and Its Applications, 15(2), 325–336. https://doi.org/10.15849/IJASCA.230720.22
Elfatimi, E., Eryigit, R., & Elfatimi, L. (2022). Beans Leaf Diseases Classification Using MobileNet Models. IEEE Access, 10, 9471–9482. https://doi.org/10.1109/ACCESS.2022.3142817
Girdhar, A. (2022). Classification of White blood cell using Convolution Neural Network. Biomedical Signal Processing and Control, 71. https://doi.org/10.1016/j.bspc.2021.103156
Hestiningsih, I. (2023). Mobile Skin Disease Classification using MobileNetV2 and NASNetMobile. International Journal on Advanced Science Engineering and Information Technology, 13(4), 1472–1479. https://doi.org/10.18517/ijaseit.13.4.18290
Li, Z. (2025). The association between white blood cell counts and metabolic health obesity among US adults. Frontiers in Nutrition, 12. https://doi.org/10.3389/fnut.2025.1458764
Liu, Y. (2020). Association of the Total White Blood Cell, Neutrophils, and Monocytes Count With the Presence, Severity, and Types of Carotid Atherosclerotic Plaque. Frontiers in Medicine, 7. https://doi.org/10.3389/fmed.2020.00313
Mahalaxmi, A. (2025). Alzheimer’s Disease Multi-task Classification Using ResNet, MobileNet. Lecture Notes in Networks and Systems, 1200, 645–656. https://doi.org/10.1007/978-981-97-9926-8_49
Mamadou, D. (2023). Cocoa Pods Diseases Detection by MobileNet Confluence and Classification Algorithms. International Journal of Advanced Computer Science and Applications, 14(9), 344–352. https://doi.org/10.14569/IJACSA.2023.0140937
Ryan, K. (2025). White blood cell estimates correlate to measures of population and individual health in an endangered population of Marbled Murrelets (Brachyramphus marmoratus). Frontiers in Veterinary Science, 12. https://doi.org/10.3389/fvets.2025.1545905
Sang, X. (2024). A real-time and high-performance MobileNet accelerator based on adaptive dataflow scheduling for image classification. Journal of Real Time Image Processing, 21(1). https://doi.org/10.1007/s11554-023-01378-5
Tahiri, M. A. (2023). White blood cell automatic classification using deep learning and optimized quaternion hybrid moments. Biomedical Signal Processing and Control, 86. https://doi.org/10.1016/j.bspc.2023.105128
Tammanashastri, P. R. (2024). Deep Learning-Based Analysis of Pediatric Pneumonia Detection in Children using Fine-tuned NasNetMobile Model. 3rd International Conference on Communication Control and Intelligent Systems Ccis 2024. https://doi.org/10.1109/CCIS63231.2024.10931870
Wang, L. (2023). Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet. Diagnostics, 13(6). https://doi.org/10.3390/diagnostics13061067
Zaki, S. Z. M. (2020). Classification of tomato leaf diseases using mobilenet v2. Iaes International Journal of Artificial Intelligence, 9(2), 290–296. https://doi.org/10.11591/ijai.v9.i2.pp290-296
Zou, Y., Wu, L., Zuo, C., Chen, L., Zhou, B., & Zhang, H. (2025). White blood cell classification network using MobileNetv2 with multiscale feature extraction module and attention mechanism. Biomedical Signal Processing and Control, 99, 106820. https://doi.org/https://doi.org/10.1016/j.bspc.2024.106820
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