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All Journal Jurnal Ilmiah Peuradeun Jurnal Ilmiah Cendekia Eksakta Indonesian Journal of Data and Science AKSELERASI: Jurnal Ilmiah Nasional SISFOTENIKA NuCSJo : Nusantara Community Service Journal
Emy Susanti
Jurusan S1 Farmasi, Fakultas Farmasi, Universitas Wahid Hasyim Jl. Menoreh Tengah X/22, Sampangan, Semarang 50236
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Journal : Indonesian Journal of Data and Science

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Deep Learning-Based Blood Cell Image Classification Using ResNet18 Architecture Edyson Tarigan, Thomas; Prasetyo, Agung Budi; Susanti, Emy
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.300

Abstract

The classification of white blood cells (WBC) plays a critical role in haematological diagnostics, yet manual examination remains a labour-intensive and subjective process. In response to this challenge, this study investigates the application of deep learning, specifically the ResNet18 convolutional neural network architecture, for the automated classification of blood cell images into four classes: eosinophils, lymphocytes, monocytes, and neutrophils. The dataset used comprises microscopic images annotated by cell type and is divided into training and validation sets with an 80:20 ratio. Standard pre-processing techniques such as image normalization and augmentation were applied to enhance model robustness and generalization. The model was fine-tuned using transfer learning with pre-trained weights from ImageNet and optimized using the Adam optimizer. Performance was evaluated through a comprehensive set of metrics including accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE). The best model achieved a validation accuracy of 86.89%, with macro-averaged precision, recall, and F1-score of 0.8738, 0.8690, and 0.8688, respectively. Lymphocyte classification yielded the highest F1-score (0.9515), while eosinophils posed the greatest classification challenge, as evidenced by lower precision and higher misclassification rates in the confusion matrix. Error-based evaluation further supported the model’s consistency, with an MSE of 0.7125 and RMSE of 0.8441. These results confirm that ResNet18 is capable of learning discriminative features in complex haematological imagery, providing an efficient and reliable alternative to manual analysis. The findings suggest potential for practical implementation in clinical workflows and pave the way for further research involving multi-model ensembles or cell segmentation pre-processing for improved precision
Co-Authors Agung Budi Prasetyo Aniek Rahmaniah Aqnes Budiarti Budi Hendrarti, Dwi Windyastuti Dianti Ias Oktaviasari, Dianti Ias Dimas Prasetianto Wicaksono Edyson Tarigan, Thomas Endah Retnani Wismaningsih, Endah Retnani Endang Wahyuningsih Fithriasari, Mariatul Krisnita Dwi Jayanti, Krisnita Dwi Maul Musyafaah Musyafaah, Maul NOVIYANTI Prayitna, Adiyuda Pujiarini, Erna Hudianti Ratna Frenty Nurkhalim Robby Cokro Buwono Robby Cokro Buwono Satiti Kuntari, Satiti Sudarmanto Sudarmanto, Sudarmanto Tuti Budirahayu Ulfi Andrian Sari