A Mobile Application for Early Skin Cancer Screening and Skin Health Awareness: A Case Study of Muara Angke

Popong Setiawati; Gerry Firmansyah; Dwi Nurmawaty; Andriyanti Asianto; Raynaldi Sandy; Alnino Dio Putera; Aurel Elviolita Putri; Alandrian Surya Tantra; Syafika Zalfanissa Dila

doi:10.36378/jtos.v8i2.5182

Popong Setiawati Informatics, Faculty of Computer Science, Universitas Esa Unggul, Indonesia
Gerry Firmansyah Informatics Engineering, Faculty of Computer Science, Universitas Esa Unggul, Indonesia
Dwi Nurmawaty Public Health, Faculty of Health Sciences, Universitas Esa Unggul, Indonesia
Andriyanti Asianto Information Systems, Faculty of Computer Science, Universitas Esa Unggul, Indonesia
Raynaldi Sandy Universitas Esa Unggul
Alnino Dio Putera Undergraduate Program in Informatics Engineering, Faculty of Computer Science, Universitas Esa Unggul, Indonesia
Aurel Elviolita Putri Undergraduate Program in Informatics Engineering, Faculty of Computer Science, Universitas Esa Unggul, Indonesia
Alandrian Surya Tantra Undergraduate Program in Informatics Engineering, Faculty of Computer Science, Universitas Esa Unggul, Indonesia
Syafika Zalfanissa Dila Undergraduate Program in Informatics Engineering, Faculty of Computer Science, Universitas Esa Unggul, Indonesia

DOI: https://doi.org/10.36378/jtos.v8i2.5182

Keywords: Mobile application, Skin health screening, Artificial intelligence, m-Health application, Health awareness

Abstract

This study aimed to develop a mobile application for early skin cancer screening while simultaneously enhancing public awareness of skin health, with a case study conducted in the coastal area of Muara Angke, North Jakarta. The research was motivated by high levels of ultraviolet radiation exposure and limited access to healthcare services, which often lead to delayed skin cancer detection. An empirical quantitative approach was employed by utilizing a Convolutional Neural Network (CNN) based on the ResNet50 architecture, deployed through a cloud based infrastructure and integrated into a Flutter based mobile application. The system was designed using an edge cloud computing approach to address the computational limitations of mobile devices. The results indicated that the application effectively performed preliminary skin lesion screening, provided classification outcomes with confidence scores, and delivered accessible skin health education content. Although the application was not intended to serve as a medical diagnostic tool, it demonstrated significant potential to support early detection efforts and improve community level awareness of skin health. This study contributes to the advancement of mobile health (m-health) by emphasizing the practical implementation of AI based applications tailored to community needs.

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References

P. Tschandl et al., “Human–computer collaboration for skin cancer diagnosis,” Nature Medicine, vol. 26, no. 8, pp. 1229–1234, 2020.

L. Yu, H. Chen, and J. Qin, “Mobile-based skin lesion classification using lightweight CNN models,” Journal of Medical Imaging, 2022.

S. Rao and R. Ahmad, “User-centered design and mobile health applications for dermatology,” International Journal of Human–Computer Interaction, 2023.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.

W. Zhang, T. Liu, and S. Huang, “Deep augmentation strategies for dermatology imaging,” Medical Image Analysis, vol. 71, pp. 102025, 2021.

X. Li, Y. Zhang, and L. Zhou, “Improved augmentation techniques for skin lesion classification,” Journal of Digital Imaging, vol. 35, no. 4, pp. 812–825, 2022.

T. Khan, M. Ibrahim, and F. Rahman, “Cloud-based deployment of CNN models for mobile medical diagnostics,” Journal of Cloud Computing: Advances, Systems and Applications, vol. 13, no. 2, pp. 1–14, 2024.

H. K. Singh and P. K. Sharma, “Comparative analysis of EfficientNet and ResNet models for dermatoscopic image classification,” IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 3, pp. 1875–1885, 2025.

M. H. Nguyen, A. R. Gupta, and P. Lee, “Lightweight CNN architectures for on-device skin lesion detection,” IEEE Access, vol. 12, pp. 99321–99333, 2024.

A. D. B. Silva and E. Martinez, “Deploying cloud-integrated CNN models for mobile health diagnostics,” Journal of Cloud Computing: Advances, Systems and Applications, vol. 13, no. 2, pp. 1–15, 2024.

L. Costa, J. Mendes, and A. Silva, “Developing cross-platform mobile applications with Flutter,” Mobile Informatics Journal, vol. 12, no. 2, pp. 55–72, 2023.

S. Patel, J. Zhao, and K. L. Tan, “Explainable AI for mobile-based skin cancer diagnosis,” Computers in Biology and Medicine, vol. 168, p. 107671, 2024.

R. Gomez, F. Chen, and Y. Wang, “Human–AI collaboration for real-time skin cancer screening on smartphones,” npj Digital Medicine, vol. 7, no. 84, pp. 1–10, 2024.

J. K. Lee and S. H. Park, “Experimental AI research frameworks for medical image analysis,” IEEE Access, vol. 11, pp. 128221–128234, 2023.

M. Elgendi, C. Lovell, and D. Abbott, “Machine learning experimental design for healthcare applications,” Sensors, vol. 21, no. 22, p. 7551, 2021.

R. L. Mehta, P. Varghese, and K. Singh, “Designing user-centered interfaces for AI-powered healthcare applications,” IEEE Access, vol. 12, pp. 105321–105334, 2024. doi: 10.1109/ACCESS.2024.105321.

J. Kim, A. Kumar, and F. Torres, “Evaluating usability in mobile AI healthcare systems: A user-experience perspective,” International Journal of Human–Computer Studies, vol. 179, p. 103087, 2023. doi: 10.1016/j.ijhcs.2023.103087.

M. Chen, Z. Wang, and J. Xu, “Edge–cloud collaboration architecture for intelligent healthcare systems,” IEEE Internet of Things Journal, vol. 11, no. 2, pp. 2134–2148, 2024.

H. Zhang, F. Li, and T. Nguyen, “A modular AI pipeline for mobile-based medical imaging applications,” IEEE Access, vol. 12, pp. 114521–114533, 2024.

S. Das, R. Banerjee, and K. Huang, “Deep learning infrastructure for real-time medical diagnostics in cloud–mobile ecosystems,” ACM Transactions on Computing for Healthcare, vol. 5, no. 1, pp. 1–15, 2023.

L. Martins, A. Costa, and R. Pereira, “Applying UML modeling in mobile health application development: A design-oriented approach,” IEEE Access, vol. 12, pp. 120453–120465, 2024.

T. Nguyen and S. Patel, “Model-driven engineering and UML-based design for AI-enabled mobile systems,” Journal of Software Engineering and Applications, vol. 17, no. 2, pp. 75–89, 2023.

A. Kumar, S. R. Gupta, and L. Zhang, “UML activity diagram modeling for AI-driven mobile healthcare applications,” IEEE Access, vol. 12, pp. 125231–125245, 2024.

R. Lopez and M. Fernandes, “Workflow modeling in mobile intelligent systems using UML activity diagrams,” Journal of Systems and Software Engineering, vol. 196, pp. 112015, 2023.

J. Fernandez, L. Silva, and P. R. Costa, “UML class modeling for modular AI-driven healthcare applications,” IEEE Access, vol. 12, pp. 130512–130528, 2024.

S. Rahman and M. Choudhury, “Object-oriented software design for intelligent mobile health systems,” Journal of Systems and Software Design, vol. 119, pp. 88–101, 2023.