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

Creative Commons License

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

MULTIMEDIA TOOLS AND APPLICATIONS, cilt.83, sa.12, ss.35995-36011, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 83 Sayı: 12
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s11042-023-17041-x
  • Dergi Adı: MULTIMEDIA TOOLS AND APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, FRANCIS, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.35995-36011
  • Anahtar Kelimeler: Deep learning, m-Health, Machine learning, Melanoma, Skin lesion analysis
  • İstanbul Kültür Üniversitesi Adresli: Evet

Özet

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.