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Front. Comms. Net.
Sec. Data Science for Communications
Volume 5 - 2024 | doi: 10.3389/frcmn.2024.1376191

Health of Things Melanoma Detection System (HTMDS) -Detection and segmentation of melanoma in dermoscopic images applied to edge computing using deep learning and fine-tuning models Provisionally Accepted

José Jerovane da Costa Nascimento1* Adriell Gomes Marques2 Yasmim Osório Adelino Rodrigues2 Guilherme Freire Brilhante Severiano2 Icaro de Sousa Rodrigues2  Carlos Dourado Jr2  LUÍS FABRÍCIO DE FREITAS SOUZA3*
  • 1Federal University of Ceara, Brazil
  • 2Federal Institute of Education, Science and Technology of Santa Catarina, Brazil
  • 3Universidade Federal do Cariri, Brazil

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Skin cancer is a pathology that has been alarmingly developing worldwide. According to the World Health Organization (WHO), melanoma is a type of cancer that affects people globally in different parts of the human body, leading to the death of thousands each year worldwide.Intelligent diagnostic tools through automatic detection in medical images are extremely effective in assisting medical diagnosis. Systems based on Computer Aided Diagnostics (CADs) are extremely important for pre-diagnosis by images, and the use of tools based on artificial intelligence for monitoring, detection, and segmentation of the pathological region are increasingly used in intelligent solutions integrated into smart city systems, through cloud data processing with the use of edge computing. This study proposes a new approach for smart cities capable of being integrated into computational systems for monitoring and assisting medical diagnosis, a fully automatic intelligent system based on the Internet of Things (IoT) for health systems in smart cities. Named Health of Things Melanoma Detection (HTMDS), the method presents a deep learning approach based on the Yolo V8x network for detecting melanomas in dermatoscopic images of melanoma, proposing new fine-tuning techniques for segmentation of the melanoma region. The new approach achieved satisfactory results above 98% accuracy for detection and above 99% accuracy for skin cancer segmentation, surpassing different works found in the state of the art in various methods, including manual, semi-automatic, and automatic approaches.

Keywords: Melanoma Detection and Segmentation, deep learning, Medical image, Fine-tuning, Edge computing

Received: 25 Jan 2024; Accepted: 12 Apr 2024.

Copyright: © 2024 da Costa Nascimento, Marques, Adelino Rodrigues, Brilhante Severiano, Rodrigues, Dourado Jr and DE FREITAS SOUZA. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
PhD. José Jerovane da Costa Nascimento, Federal University of Ceara, Fortaleza, 60020-181, Ceará, Brazil
MD, PhD. LUÍS FABRÍCIO DE FREITAS SOUZA, Universidade Federal do Cariri, Juazeiro do Norte, Brazil