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

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.

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Publication Details

Published
2024-09-21
DOI
https://doi.org/10.1109/idap64064.2024.10710812
Primary Topic
AI in cancer detection
Type
article
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article

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

Halis Altun, Nazlı Tokatli, Gürkan Göztepeli, Yakuphan Bılmez et al.
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AI in cancer detection
article

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

Halis Altun, Nazlı Tokatli, Gürkan Göztepeli, Yakuphan Bılmez, Muhammed Güler, Furkan Karan
article en

Abstract

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.

Istanbul Health and Technology University
Industry, innovation and infrastructure
Openalex Percentile: Top 84%
AI in cancer detection
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