Tokatlı, NazlıBilmez, YakuphanGöztepeli, GürkanGüler, MuhammedKaran, FurkanAltun, Halis2024-10-232024-10-232024Tokatlı, N., Bilmez, Y., Göztepeli, G., Güler, M., Karan, F. & Altun, H. (2024). MelanoTech: Development of a mobile application infrastructure for melanoma cancer diagnosis based on artificial intelligence technologies. 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1-6. Malatya, Turkiye. https://doi.org/10.1109/IDAP64064.2024.10710812https://doi.org/10.1109/IDAP64064.2024.10710812https://hdl.handle.net/20.500.13055/834This preliminary work introduces MelanoTech, a mHealth application designed and implemented to offer a user-friendly and intuitive interface for the early diagnosis of melanoma, a kind of skin cancer with significant fatality rates [1]. The application demonstrates promising performance in segmentation and classification tasks by utilizing deep learning models with Generative Adversarial Networks (GANs) for data augmentation. MelanoTech achieves a comprehensive accuracy rate of 92%, with a segmentation model accuracy rate of 93% and a lesion detection accuracy rate of 90%. Finally, incorporating data augmentation approaches based on GANs resulted in a 5% enhancement in the model’s performance. These findings highlight the capacity of MelanoTech as a dependable tool for improving the early diagnosis of melanoma and decreasing the workload of physicians in Turkish public hospitals.eninfo:eu-repo/semantics/closedAccessArtificial Intelligence applicaitons in Healthcare SystemsMelanoma DetectionMobile Health ApplicationsDeep Learning TechniquesDermoscopic ImagingConference Object10.1109/IDAP64064.2024.10710812162-s2.0-85207880802