EVALUATION OF PRE-TRAINED CONVOLUTIONAL ARCHITECTURES FOR ORAL CANCER DETECTION
DOI:
https://doi.org/10.66104/eecb2s81Keywords:
Deep Learning, Computer Vision, Oral Cancer, Convolutional Neural NetworkAbstract
Oral cancer remains one of the major public health challenges, especially in developing countries, where late detection contributes significantly to the high mortality associated with the disease. Automated methods based on Computer Vision and Deep Learning have shown great potential in supporting early diagnosis by providing objective assistance to clinical evaluation. In this study, the performance of three widely established pre-trained convolutional architectures in the literature—DenseNet121, GoogLeNet, and ResNet18—was investigated for the binary classification of intraoral images using a public dataset containing 950 samples. The study employs transfer learning and 5-fold cross-validation, enabling an analysis of the models’ generalization capability. The results show that DenseNet121 achieved the best performance among the evaluated architectures (F1-score = 0.9510), standing out particularly in sensitivity and overall balance across the metrics. These findings reinforce the potential of CNNs as complementary tools in the screening process of oral lesions, highlighting their future applicability in real clinical settings.
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Copyright (c) 2026 Maria Isabelly de Brito Rodrigues, Wanderson de Vasconcelos Rodrigues da Silva, Ricardo Moura Sekeff Budaruiche, Iallen Gabio de Sousa Santos
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