Artificial intelligence in tongue diagnosis: classification of tongue lesions and normal tongue images using deep convolutional neural network.

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Tiryaki B., Torenek-Agirman K., Miloglu Ö., Korkmaz B., Ozbek İ. Y., Oral E. A.

BMC medical imaging, cilt.24, sa.1, ss.59, 2024 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1186/s12880-024-01234-3
  • Dergi Adı: BMC medical imaging
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Biotechnology Research Abstracts, EMBASE, MEDLINE, Directory of Open Access Journals
  • Sayfa Sayıları: ss.59
  • Atatürk Üniversitesi Adresli: Evet

Özet

ObjectiveThis study aims to classify tongue lesion types using tongue images utilizing Deep Convolutional Neural Networks (DCNNs).MethodsA dataset consisting of five classes, four tongue lesion classes (coated, geographical, fissured tongue, and median rhomboid glossitis), and one healthy/normal tongue class, was constructed using tongue images of 623 patients who were admitted to our clinic. Classification performance was evaluated on VGG19, ResNet50, ResNet101, and GoogLeNet networks using fusion based majority voting (FBMV) approach for the first time in the literature.ResultsIn the binary classification problem (normal vs. tongue lesion), the highest classification accuracy performance of 93,53% was achieved utilizing ResNet101, and this rate was increased to 95,15% with the application of the FBMV approach. In the five-class classification problem of tongue lesion types, the VGG19 network yielded the best accuracy rate of 83.93%, and the fusion approach improved this rate to 88.76%.ConclusionThe obtained test results showed that tongue lesions could be identified with a high accuracy by applying DCNNs. Further improvement of these results has the potential for the use of the proposed method in clinic applications.