Artificial intelligence algorithm for the histopathological diagnosis of skin cancer

Autores

  • Victor Antonio Kuiava Faculdade de Medicina – Universidade de Medicina, Passo Fundo, RS - Brasil
  • Eliseu Luiz Kuiava Departamento de Engenharia Elétrica – Universidade Internacional, São Miguel do Oeste, SC – Brasil
  • Eduardo Ottobelli Chielle Departamento de Ciências da Vida - Universidade do Oeste de Santa Catarina, São Miguel do Oeste, SC - Brasil.
  • Francisco Madalosso De Bittencourt Faculdade de Medicina – Universidade de Medicina, Passo Fundo, RS - Brasil Hospital de Clinicas de Passo Fundo, Passo Fundo, RS - Brasil.

Palavras-chave:

Skin lesions, Artificial Intelligence, Diagnosis, Neoplasms, Melanoma.

Resumo

Introduction: Cutaneous neoplasms are the most common cancers in the world, and have high morbidity rates. A definitive diagnosis can only be obtained after histopathological evaluation of the lesions. Objective: To develop an artificial intelligence program to establish the histopathological diagnosis of cutaneous lesions. Methodology: A deep learning program was built using three neural network architectures: MobileNet, Inception and convolutional networks. A database was constructed using 2732 images of melanomas, basal and squamous cell carcinomas, and normal skin. The validation set consisted of 284 images from all 4 categories, allowing for the calculation of sensitivity and specificity. All images were provided by the Path Presenter website. Results: The sensitivity and specificity of the MobileNet model were 92% (95%CI, 83-100%) and 97% (95%CI, 90-100%), respectively; corresponding figures for the Inception model were 98.3% (95%CI, 86-100%) and 98.8% (95%CI, 98.2-100%); lastly, the sensitivity and specificity of the convolutional network model were 91.6% (95%CI, 73.8-100%) and 95.7% (95%CI, 94.4-97.2%). The maximum sensitivity for the differentiation of malignant conditions was 91%, and specificity was 95.4%. Conclusion: The program developed in the present study can efficiently distinguish between the main types of skin cancer with high sensitivity and specificity.

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Publicado

2021-04-13

Como Citar

1.
Kuiava VA, Kuiava EL, Chielle EO, De Bittencourt FM. Artificial intelligence algorithm for the histopathological diagnosis of skin cancer. Clin Biomed Res [Internet]. 13º de abril de 2021 [citado 11º de agosto de 2022];40(4). Disponível em: https://www.seer.ufrgs.br/index.php/hcpa/article/view/108236

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