Development of A Website-Integrated MobileNet Model for Ischemic Stroke Detection Based on CT Scan Images
https://doi.org/10.53770/medica.v8i5.955
Keywords
Ischemic Stroke MobileNet Website CT scanAbstract
The prevalence of ischemic stroke in Indonesia continues to increase, highlighting the need for rapid and accurate diagnostic approaches. Computed Tomography (CT) imaging plays a crucial role in the early detection of ischemic stroke, particularly within the therapeutic window of 3–4.5 hours after symptom onset. This study aimed to develop and evaluate a website-based ischemic stroke detection system using a MobileNet deep learning model integrated with CT scan image analysis. A Research and Development (R&D) approach employing the Rapid Application Development (RAD) framework was applied. The model was developed using 2,000 CT scan images obtained from a Kaggle dataset, comprising 1,000 ischemic stroke images and 1,000 normal control images. The dataset was divided into training and validation sets, with 800 images per class used for training and 200 images per class used for validation. External clinical validation was conducted using 600 CT scan images collected from hospitals, consisting of 300 ischemic stroke images and 300 normal control images. Model performance was evaluated using accuracy, precision, sensitivity, and specificity metrics, while system usability was assessed using the System Usability Scale (SUS). The developed model achieved an accuracy of 92.25%, precision of 92.00%, sensitivity of 92.50%, and specificity of 92.00% on the validation dataset. However, performance decreased during external clinical testing, yielding an accuracy of 71.50%, precision of 73.12%, sensitivity of 68.00%, and specificity of 75.00%. These findings are consistent with previous studies indicating that models trained solely on public datasets often experience performance degradation when applied to real-world clinical data, emphasizing the importance of multi-institutional training datasets and extensive external validation. The usability evaluation produced an average SUS score of 83, indicating excellent user acceptance and system usability. The proposed website-based MobileNet model demonstrates strong potential as an accessible tool for supporting early ischemic stroke detection from CT scan images and may be further enhanced through the incorporation of multi-slice or volumetric imaging data to improve clinical performance.
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Albarrak, K., Gulzar, Y., Hamid, Y., Mehmood, A., & Soomro, A. B. (2022). A Deep Learning-Based Model for Date Fruit Classification. Sustainability, 14(10), 6339. https://doi.org/10.3390/su14106339
Alberto, I. R. I., Alberto, N. R. I., Ghosh, A. K., Jain, B., Jayakumar, S., Martinez-Martin, N., McCague, N., Moukheiber, D., Moukheiber, L., Moukheiber, M., Moukheiber, S., Yaghy, A., Zhang, A., & Celi, L. A. (2023). The impact of commercial health datasets on medical research and health-care algorithms. The Lancet, 5(5), e288–e294. https://doi.org/10.1016/S2589-7500(23)00025-0
Alharbi, T. A. F., Rababa, M., Alsuwayl, H., Alsubail, A., & Alenizi, W. S. (2025). Diagnostic Challenges and Patient Safety: The Critical Role of Accuracy - A Systematic Review. Journal of multidisciplinary healthcare, 18, 3051–3064. https://doi.org/10.2147/JMDH.S512254
Alshamari, M. (2016). Usability Factors Assessment in Health Information System. Intelligent Information Management, 8(6), 170–180. https://doi.org/10.4236/iim.2016.86012
Alshardan, A., Mahgoub, H., Alruwais, N., Darem, A. A., Almukadi, W. S., & Mohamed, A. (2024). Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection. Scientific reports, 14(1), 18478. https://doi.org/10.1038/s41598-024-69415-2
Archana, R., & Jeevaraj, P. S. E. (2024). Deep learning models for digital image processing: a review. Artificial Intelligence Review, 57,11. https://doi.org/10.1007/s10462-023-10631-z
Bequet, A. Y., Kartikasari, Y., Mulyati, S., Isnoviasih, S. T., Teknik, J., Dan, R., Poltekkes, R., & Semarang, K. (2019). Analisis Image Quality CT Scan Thorax Dengan Variasi Lung Window Kernel pada MSCT Siemens Somatom Emotion 6. Jurnal Imejing Diagnostik (JImeD), 5, 55–59. http://ejournal.poltekkes-smg.ac.id/ojs/index.php/jimed/index
Bichri, H., Chergui, A., & Hain, M. (2024). Investigating the Impact of Train / Test Split Ratio on the Performance of Pre-Trained Models with Custom Datasets. In IJACSA) International Journal of Advanced Computer Science and Applications, 15(2), 331-339.
Chen, W., Wu, J., Wei, R., Wu, S., Xia, C., Wang, D., Liu, D., Zheng, L., Zou, T., Li, R., Qi, X., & Zhang, X. (2022). Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study. Insights into imaging, 13(1), 184. https://doi.org/10.1186/s13244-022-01331-3
Dewi, L., & Fitraneti, E. (2024). (2024). Stroke Iskemik. Scientific Journal, 3(6), 379–388. https://doi.org/10.56260/sciena.v3i6.173
Feigin, V., Stark, B.A., Johnson, C.O., Roth, G.A., Bisignano, C., Abady, G.G., &…, Murray, C.J.L. (2021). Global, regional, and national burden of stroke and its risk factors, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet Neurology, 20(10), 1–26. https://doi.org/10.1016/S1474-4422(21)00252-0
Gentili, E., Franchini, G., Zese, R., Alberti, M., Ferrara, M., Domenicano, I., & Grassi, L. (2024). Machine learning from real data: A mental health registry case study. Computer Methods and Programs in Biomedicine Update, 5, 100132. https://doi.org/10.1016/j.cmpbup.2023.100132
Gitawiarsa, I. P. P. W., Guruh, B., Dartini, D., Mulyantoro, D. K., Masrochah, S., & Wibowo, G. M. (2026). Evaluasi Efektivitas Model U-Net untuk Segmentasi Citra Renal Scintigraphy pada Penilaian Fungsi Ginjal. MAHESA : Malahayati Health Student Journal, 6(4), 106–119. https://doi.org/10.33024/mahesa.v6i4.22074
Gleixner, A., Hendel, G., Gamrath, G., Achterberg, T., Bastubbe, M., Berthold, T., Christophel, P., Jarck, K., Koch, T., Linderoth, J., Lübbecke, M., Mittelmann, H. D., Ozyurt, D., Ralphs, T. K., Salvagnin, D., & Shinano, Y. (2021). MIPLIB 2017: data-driven compilation of the 6th mixed-integer programming library. Mathematical Programming Computation, 13(3), 443–490. https://doi.org/10.1007/s12532-020-00194-3
Guo, L. L., Pfohl, S. R., Fries, J., Johnson, A. E. W., Posada, J., Aftandilian, C., Shah, N., & Sung, L. (2022). Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine. Scientific reports, 12(1), 2726. https://doi.org/10.1038/s41598-022-06484-1
Gurcan, F., & Soylu, A. (2024). Learning from Imbalanced Data: Integration of Advanced Resampling Techniques and Machine Learning Models for Enhanced Cancer Diagnosis and Prognosis. Cancers, 16(19), 3417. https://doi.org/10.3390/cancers16193417
Hartanto, W. A. (2024). Correlation Between Blood Glucose Levels And Risk Of Non-Hemorrhagic Stroke_ Analysis of A Prospective Cohort Study In An Elderly Population. Health Sains, 5(4), 284–292.
Hayati, N., Sudiyono, S., Handoko, B. D., Budiati, T. A., Kartikasari, Y., & Wibowo, G. M. (2024). Transforming CT-Scan Scheduling System in Hospital Through Web-Based Technology. JMMR (Jurnal Medicoeticolegal Dan Manajemen Rumah Sakit), 13(3), 332–346. https://doi.org/10.18196/jmmr.v13i3.463
Herpich, F., & Rincon, F. (2020). Management of Acute Ischemic Stroke. Critical care medicine, 48(11), 1654–1663. https://doi.org/10.1097/CCM.0000000000004597
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Computer Vision and Pattern Recognition, 1-9. http://arxiv.org/abs/1704.04861
Hussain, S., Mubeen, I., Ullah, N., Shah, S. S. U. D., Khan, B. A., Zahoor, M., Ullah, R., Khan, F. A., & Sultan, M. A. (2022). Modern Diagnostic Imaging Technique Applications and Risk Factors in the Medical Field: A Review. BioMed research international, 2022, 5164970. https://doi.org/10.1155/2022/5164970
Hyzy, M., Bond, R., Mulvenna, M., Bai, L., Dix, A., Leigh, S., & Hunt, S. (2022). System Usability Scale Benchmarking for Digital Health Apps: Meta-analysis. JMIR mHealth and uHealth, 10(8), e37290. https://doi.org/10.2196/37290
Ishariani, L., & Rachmania, D. (2021). Hubungan Respon Time Keluarga dalam Membawa Pasien Stroke ke Pelayanan Kesehatan dengan Tingkat Keparahan Pasien Stroke. The Indonesian Journal of Health Science, 13(1), 35–43. https://doi.org/10.32528/ijhs.v13i1.5274
Kovács, K. B., Bencs, V., Hudák, L., Oláh, L., & Csiba, L. (2023). Hemorrhagic Transformation of Ischemic Strokes. International journal of molecular sciences, 24(18), 14067. https://doi.org/10.3390/ijms241814067
Lestari, D., Wibowo, G.M., Setiawan, A.N., Suwondo, A., & Susanto, E. (2026). Model Deteksi Parkinson Disease Berbasis Deep Learning Menggunakan Arsitektur VGG. Journal Imeging Diagnostik, 12(1), 34–39. https://doi.org/10.31983/jimed.v12i1.14284
Li, L., Yu, H., & Kunc, M. (2024). The impact of forum content on data science open innovation performance: A system dynamics-based causal machine learning approach. Technological Forecasting and Social Change, 198, 1-22. https://doi.org/10.1016/j.techfore.2023.122936
Liu, Y., Xu, Y., Guo, L., Chen, Z., Xia, X., Chen, F., Tang, L., Jiang, H., & Xie, C. (2025). Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study. BMC medical informatics and decision making, 25(1), 248. https://doi.org/10.1186/s12911-025-03082-9
Miseta, T., Fodor, A., & Vathy-Fogarassy, Á. (2024). Surpassing early stopping: A novel correlation-based stopping criterion for neural networks. Neurocomputing, 567, 127028. https://doi.org/10.1016/j.neucom.2023.127028
Nukovic, J. J., Opancina, V., Ciceri, E., Muto, M., Zdravkovic, N., Altin, A., Altaysoy, P., Kastelic, R., Velazquez Mendivil, D. M., Nukovic, J. A., Markovic, N. V., Opancina, M., Prodanovic, T., Nukovic, M., Kostic, J., & Prodanovic, N. (2023). Neuroimaging Modalities Used for Ischemic Stroke Diagnosis and Monitoring. Medicina (Kaunas, Lithuania), 59(11), 1908. https://doi.org/10.3390/medicina59111908
Nurfadhillah, D., Santoso, G., Fatimah, Murti Wibowo, G., Darmini, & Nuryatno. (2023). Effectiveness of Automatic Detection of Osteoarthritis using Convolutional Neural Network (CNN) Method with DenseNet201 on Digital Images of Knee Joint Radiography. E3S Web of Conferences, 448, 1-10. https://doi.org/10.1051/e3sconf/202344802052
Ogundokun, R.O., Misra, S., Akinrotimi, A.O., Hasan Ogul, H. (2023). MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors. Sensors, 23(2), 656. https://doi.org/10.3390/s23020656
Pan, Y., Zhang, H., Yang, J., Guo, J., Yang, Z., Wang, J., & Song, G. (2021). Identification and Diagnosis of Cerebral Stroke through Deep Convolutional Neural Network-Based Multimodal MRI Images. Contrast media & molecular imaging, 2021, 7598613. https://doi.org/10.1155/2021/7598613
Pu, L., Wang, L., Zhang, R., Zhao, T., Jiang, Y., & Han, L. (2023). Projected Global Trends in Ischemic Stroke Incidence, Deaths and Disability-Adjusted Life Years From 2020 to 2030. Stroke, 54(5), 1330–1339. https://doi.org/10.1161/STROKEAHA.122.040073
Rahimzadeh, M., Askari, A., Parvin, S., Safi, E., & Mohammadi, M. R. (2024). Wise-SrNet: A Novel Architecture for Enhancing Image Classification by Learning Spatial Resolution of Feature Maps. Berlin: Springer-Verlag. https://doi.org/10.1007/s10044-024-01211-0
Rockenschaub, P., Hilbert, A., Kossen, T., Elbers, P., Von Dincklage, F., Madai, V. I., & Frey, D. (2024). The Impact of Multi-Institution Datasets on the Generalizability of Machine Learning Prediction Models in the ICU. Critical Care Medicine, 52(11), 1710–1721. https://doi.org/10.1097/CCM.0000000000006359
Salehi, A. W., Khan, S., Gupta, G., Alabduallah, B. I., Almjally, A., Alsolai, H., Siddiqui, T., & Mellit, A. (2023). A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope. In Sustainability (Switzerland), 15(7), 5930. https://doi.org/10.3390/su15075930
Schmidt, J., Cerqueira, T. F. T. ; Romero, A. H., Loew, A. , Jäger, F., Wang, H.-C., Botti, S., Marques, M. A. L. (2024). Improving machine-learning models in materials science through large datasets. Materials Today Physics, 48 (101560), 1-11. https://doi.org/10.3929/ethz-b-000698132
Simola, S., Hörhammer, I., Xu, Y., Bärkås, A., Fagerlund, A. J., Hagström, J., Holmroos, M., Hägglund, M., Johansen, M. A., Kane, B., Kharko, A., Scandurra, I., & Kujala, S. (2023). Patients' Experiences of a National Patient Portal and Its Usability: Cross-Sectional Survey Study. Journal of medical Internet research, 25, e45974. https://doi.org/10.2196/45974
Van der Nat, D. J., Huiskes, V. J. B., Taks, M., Pouls, B. P. H., van den Bemt, B. J. F., & van Onzenoort, H. A. W. (2022). Usability and perceived usefulness of patient-centered medication reconciliation using a personalized health record: a multicenter cross-sectional study. BMC health services research, 22(1), 776. https://doi.org/10.1186/s12913-022-07967-7
Wahyuningsih, E. P., & Martiwi, I. N. A. (2025). Diversity of Bryophytes Based on Substrate Types in the Karangkamulyan Tourist Area, Ciamis Regency. Jurnal Biologi Tropis, 25(1), 517-528. https://doi.org/10.29303/jbt.v25i1.8495
Zantvoort, K., Nacke, B., Görlich, D., Hornstein, S., Jacobi, C., & Funk, B. (2024). Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions. NPJ digital medicine, 7(1), 361. https://doi.org/10.1038/s41746-024-01360-w
Zhu, H., Hu, S., Li, Y., Sun, Y., Xiong, X., Hu, X., Chen, J., & Qiu, S. (2022). Interleukins and Ischemic Stroke. Frontiers in immunology, 13, 828447. https://doi.org/10.3389/fimmu.2022.828447
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