Diagnosis of skin diseases in the era of deep learning and mobile technology

GÖÇERİ E.

COMPUTERS IN BIOLOGY AND MEDICINE, cilt.134, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 134
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.compbiomed.2021.104458
  • Dergi Adı: COMPUTERS IN BIOLOGY AND MEDICINE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, CINAHL, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, Library, Information Science & Technology Abstracts (LISTA), MEDLINE
  • Anahtar Kelimeler: Skin disease, Mobile application, Deep learning, Lightweight network, MobileNet, Lesion classification, CONVOLUTIONAL NEURAL-NETWORK, LEVEL SET, EVOLUTION
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Efficient methods developed with deep learning in the last ten years have provided objectivity and high accuracy in the diagnosis of skin diseases. They also support accurate, cost-effective and timely treatment. In addition, they provide diagnoses without the need to touch patients, which is very desirable when the disease is contagious or the patients have another contagious disease. On the other hand, it is not possible to run deep networks on resource-constrained devices (e.g., mobile phones). Therefore, lightweight network architectures have been proposed in the literature. However, merely a few mobile applications have been developed for the diagnosis of skin diseases from colored photographs using lightweight networks. Moreover, only a few types of skin diseases have been addressed in those applications. Additionally, they do not perform as well as the deep network models, particularly for pattern recognition. Therefore, in this study, a novel model has been constructed using MobileNet. Also, a novel loss function has been developed and used. The main contributions of this study are: (i) proposing a novel hybrid loss function; (ii) proposing a modified-MobileNet architecture; (iii) designing and implementing a mobile phone application with the modified-MobileNet and a user-friendly interface. Results indicated that the proposed technique can diagnose skin diseases with 94.76% accuracy.