Design and implementation of a deep learning-empowered m-Health application

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AKBULUT A., Desouki S., AbdelKhaliq S., Khantomani L., ÇATAL Ç.

MULTIMEDIA TOOLS AND APPLICATIONS, vol.83, no.12, pp.35995-36011, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 83 Issue: 12
  • Publication Date: 2024
  • Doi Number: 10.1007/s11042-023-17041-x
  • Journal Name: MULTIMEDIA TOOLS AND APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, FRANCIS, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.35995-36011
  • Keywords: Deep learning, m-Health, Machine learning, Melanoma, Skin lesion analysis
  • Istanbul Kültür University Affiliated: Yes

Abstract

Many people are unaware of the severity of melanoma disease even though such a disease can be fatal if not treated early. This research aims to facilitate the diagnosis of melanoma disease in people using a mobile health application because some people do not prefer to visit a dermatologist due to several concerns such as feeling uncomfortable by exposing their bodies. As such, a skincare application was developed so that a user can easily analyze a mole at any part of the body and get the diagnosis results quickly. In the first phase, the corresponding image is extracted and sent to a web service. Later, the web service classifies using the pre-trained model built based on a deep learning algorithm. The final phase displays the confidence rates on the mobile application. The proposed model utilizes the Convolutional Neural Network and provides 84% accuracy and 72% precision. The results demonstrate that the proposed model and the corresponding mobile application provide remarkable results for addressing the specified health problem.