Journal of Technology and Informatics
Cloud-Based Mobile Application for Skin Lesion Screening Using InceptionV3
DOI:
https://doi.org/10.37802/joti.v8i2.1346Keywords:
Skin Lesion Classification, Mobile Application, Cloud Computing, InceptionV3, Flutter, Google Cloud PlatformAbstract
Skin-lesion studies have achieved promising classification accuracy; however, their implementation in mobile–cloud environments remains limited. This study presents Skin Alert, a cloud-based prototype for classifying seven skin-lesion categories in the HAM10000 dataset using an ImageNet-pretrained InceptionV3 model. Hyperparameters, including learning rate, dropout rate, batch size, and number of epochs, were optimized using grid search. The original class distribution was retained, while data augmentation and class-weighted learning were applied during training. The final model achieved an internal validation accuracy of 85.32%, weighted precision of 85.79%, weighted recall of 85.32%, weighted F1-score of 85.38%, macro F1-score of 72.93%, and macro-average one-vs-rest AUC of 96.84%. The model was deployed through a Flask API on Google Cloud Platform and integrated with a Flutter application, Firebase Authentication, Realtime Database, and Storage. Black-box and backend integration testing confirmed that predefined workflows operated as intended. The locally hosted API achieved approximately 250–300 ms response time per image, while cloud deployment required approximately 1.10 s per image. These findings demonstrate the technical feasibility of the proposed mobile–cloud prototype.
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References
[1] World Health Organization, “Ultraviolet radiation,” Jun. 21, 2022. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/ultraviolet-radiation. [Accessed: Aug. 17, 2026].
[2] J. Vieira, F. Mendonça, and F. Morgado-Dias, “Deep Learning Approaches for Skin Lesion Detection,” Electronics, vol. 14, no. 14, Art. no. 2785, 2025, doi: 10.3390/electronics14142785.
[3] Y. Wu, B. Chen, A. Zeng, D. Pan, R. Wang, and S. Zhao, “Skin Cancer Classification With Deep Learning: A Systematic Review,” Frontiers in Oncology, vol. 12, Art. no. 893972, 2022, doi: 10.3389/fonc.2022.893972.
[4] B. Shetty, R. Fernandes, A. P. Rodrigues, et al., “Skin lesion classification of dermoscopic images using machine learning and convolutional neural network,” Scientific Reports, vol. 12, Art. no. 18134, 2022, doi: 10.1038/s41598-022-22644-9.
[5] T. M. Alam, K. Shaukat, W. A. Khan, I. A. Hameed, L. A. Almuqren, M. A. Raza, M. Aslam, and S. Luo, “An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset,” Diagnostics, vol. 12, no. 9, Art. no. 2115, 2022, doi: 10.3390/diagnostics12092115.
[6] B. C. R. S. Furriel et al., “Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review,” Frontiers in Medicine, vol. 10, Art. no. 1305954, 2024, doi: 10.3389/fmed.2023.1305954.
[7] E. Goceri, “Diagnosis of skin diseases in the era of deep learning and mobile technology,” Computers in Biology and Medicine, vol. 134, Art. no. 104458, 2021, doi: 10.1016/j.compbiomed.2021.104458.
[8] R. Francese, M. Frasca, M. Risi, and G. Tortora, “A mobile augmented reality application for supporting real-time skin lesion analysis based on deep learning,” Journal of Real-Time Image Processing, vol. 18, pp. 1247–1259, 2021, doi: 10.1007/s11554-021-01109-8.
[9] I. Kousis, I. Perikos, I. Hatzilygeroudis, and M. Virvou, “Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application,” Electronics, vol. 11, no. 9, Art. no. 1294, 2022, doi: 10.3390/electronics11091294.
[10] A. M. Smak Gregoor et al., “An artificial intelligence based app for skin cancer detection evaluated in a population based setting,” npj Digital Medicine, vol. 6, Art. no. 90, 2023, doi: 10.1038/s41746-023-00831-w.
[11] R. Omirgaliyev, A. Arystanbekova, A. Alzhanov, and A. Makatov, “Mobile Application of Convolutional Neural Networks for Melanoma Classification,” in 2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST), Astana, Kazakhstan, 2024, doi: 10.1109/SIST61555.2024.10629269.
[12] P. Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Scientific Data, vol. 5, Art. no. 180161, 2018, doi: 10.1038/sdata.2018.161.
[13] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2818–2826, doi: 10.1109/CVPR.2016.308.
[14] S. Aksoy, P. Demircioglu, and I. Bogrekci, “Deep Learning-Based Web Application for Automated Skin Lesion Classification and Analysis,” Dermato, vol. 5, no. 2, Art. no. 7, 2025, doi: 10.3390/dermato5020007.
[15] H. W. Huang, B. W.-Y. Hsu, C.-H. Lee, and V. S. Tseng, “Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers,” The Journal of Dermatology, vol. 48, no. 3, pp. 310–316, 2021, doi: 10.1111/1346-8138.15683.
[16] I. Valova, P. Dinh, and N. Gueorguieva, “Mobile Application for Skin Cancer Classification Using Deep Learning,” Journal of Machine Intelligence and Data Science, vol. 4, pp. 15–26, 2023, doi: 10.11159/jmids.2023.003.
[17] L. Lasminiasih, G. E. Saputra, R. B. Utomo, and E. Wiseno, “Using Prototyping Method for Analysis and Design of Information Systems for Student Registration in Sekolah Master,” International Journal Science and Technology, vol. 1, no. 2, pp. 19–29, 2022, doi: 10.56127/ijst.v1i2.140.
[18] D. J. C. Sihombing, “Exploring prototype methodology in land information system development: design and evaluation of an application,” Jurnal Info Sains: Informatika dan Sains, vol. 14, no. 1, pp. 594–604, 2024. [Online]. Available: https://ejournal.seaninstitute.or.id/index.php/InfoSains/article/view/4007.
[19] Flutter, “Development,” [Online]. Available: https://flutter.dev/development. [Accessed: Aug. 17, 2026].
[20] Google, “Firebase Documentation,” [Online]. Available: https://firebase.google.com/docs. [Accessed: Aug. 17, 2026].
[21] S. Sachdeva et al., “Unraveling the role of cloud computing in health care system and biomedical sciences,” Heliyon, vol. 10, no. 7, Art. no. e29044, 2024, doi: 10.1016/j.heliyon.2024.e29044.
[22] Object Management Group, “OMG Unified Modeling Language (OMG UML), Version 2.5.1,” Dec. 2017. [Online]. Available: https://www.omg.org/spec/UML/2.5.1/PDF.
[23] M. Anggraeni, H. Andhika F. R., H. Rante, and S. Sukaridhoto, “Indonesian Sign Language (SIBI) Learning Media Application Based on Deep Learning Technology for Deaf Children,” Journal of Technology and Informatics (JoTI), vol. 5, no. 1, pp. 41–47, 2023, doi: 10.37802/joti.v5i1.384.
[24] M. R. D. Ulhaq, M. A. Zaidan, and D. Firdaus, “Pengenalan Ekspresi Wajah Secara Real-Time Menggunakan Metode SSD Mobilenet Berbasis Android,” Journal of Technology and Informatics (JoTI), vol. 5, no. 1, pp. 48–52, 2023, doi: 10.37802/joti.v5i1.387.
[25] V. H. Sahin, I. Oztel, and G. Yolcu Oztel, “Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application,” Journal of Medical Systems, vol. 46, Art. no. 79, 2022, doi: 10.1007/s10916-022-01863-7.
[26] A. A. Abdulredah, M. A. Fadhel, L. Alzubaidi, Y. Duan, M. Kherallah, and F. Charfi, “Towards unbiased skin cancer classification using deep feature fusion,” BMC Medical Informatics and Decision Making, vol. 25, Art. no. 48, 2025, doi: 10.1186/s12911-025-02889-w.
[27] K. Manzoor, N. U. Gilal, M. Agus, and J. Schneider, “Dual-stage segmentation and classification framework for skin lesion analysis using deep neural network,” Digital Health, vol. 11, Art. no. 20552076251351858, 2025, doi: 10.1177/20552076251351858.
