MelanoTech: Development of a Mobile Application Infrastructure for Melanoma Cancer Diagnosis Based on Artificial Intelligence Technologies

• Published in: 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP) pp. 1 - 6
• Main Authors: Tokatli, Nazli, Bilmez, Yakuphan, Goztepeli, Gurkan, Guler, Muhammed, Karan, Furkan, Altun, Halis
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21-09-2024
• Subjects: Accuracy; Artificial Intelligence applicaitons in Healthcare Systems; Data augmentation; Data models; Deep Learning Techniques; Dermoscopic Imaging; Generative adversarial networks; Hospitals; Imaging; Learning (artificial intelligence); Lesions; Melanoma; Melanoma Detection; Mobile applications; Mobile Health Applications
• DOI: 10.1109/IDAP64064.2024.10710812

This 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.