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Use of Artificial Intelligence in the Classification of Elementary Oral Lesions from Clinical Images

Author

Listed:
  • Rita Fabiane Teixeira Gomes

    (Department of Oral Pathology, Faculdade de Odontologia, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 90035-003, Brazil)

  • Jean Schmith

    (Polytechnic School, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
    Technology in Automation and Electronics Laboratory—TECAE Lab, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil)

  • Rodrigo Marques de Figueiredo

    (Polytechnic School, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil
    Technology in Automation and Electronics Laboratory—TECAE Lab, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil)

  • Samuel Armbrust Freitas

    (Department of Applied Computing, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil)

  • Giovanna Nunes Machado

    (Polytechnic School, University of Vale do Rio dos Sinos—UNISINOS, São Leopoldo 93022-750, Brazil)

  • Juliana Romanini

    (Oral Medicine, Otorhynolaringology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-003, Brazil)

  • Vinicius Coelho Carrard

    (Department of Oral Pathology, Faculdade de Odontologia, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 90035-003, Brazil
    Oral Medicine, Otorhynolaringology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre 90035-003, Brazil
    TelessaudeRS, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 91501-970, Brazil)

Abstract

Objectives: Artificial intelligence has generated a significant impact in the health field. The aim of this study was to perform the training and validation of a convolutional neural network (CNN)-based model to automatically classify six clinical representation categories of oral lesion images. Method: The CNN model was developed with the objective of automatically classifying the images into six categories of elementary lesions: (1) papule/nodule; (2) macule/spot; (3) vesicle/bullous; (4) erosion; (5) ulcer and (6) plaque. We selected four architectures and using our dataset we decided to test the following architectures: ResNet-50, VGG16, InceptionV3 and Xception. We used the confusion matrix as the main metric for the CNN evaluation and discussion. Results: A total of 5069 images of oral mucosa lesions were used. The oral elementary lesions classification reached the best result using an architecture based on InceptionV3. After hyperparameter optimization, we reached more than 71% correct predictions in all six lesion classes. The classification achieved an average accuracy of 95.09% in our dataset. Conclusions: We reported the development of an artificial intelligence model for the automated classification of elementary lesions from oral clinical images, achieving satisfactory performance. Future directions include the study of including trained layers to establish patterns of characteristics that determine benign, potentially malignant and malignant lesions.

Suggested Citation

  • Rita Fabiane Teixeira Gomes & Jean Schmith & Rodrigo Marques de Figueiredo & Samuel Armbrust Freitas & Giovanna Nunes Machado & Juliana Romanini & Vinicius Coelho Carrard, 2023. "Use of Artificial Intelligence in the Classification of Elementary Oral Lesions from Clinical Images," IJERPH, MDPI, vol. 20(5), pages 1-14, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:3894-:d:1076616
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    References listed on IDEAS

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    1. Emmanuel Ovalle-Magallanes & Juan Gabriel Avina-Cervantes & Ivan Cruz-Aceves & Jose Ruiz-Pinales, 2020. "Transfer Learning for Stenosis Detection in X-ray Coronary Angiography," Mathematics, MDPI, vol. 8(9), pages 1-20, September.
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