Automatic classification and detection of oral cancer in photographic images using deep learning algorithms

Abstract

Background Oral cancer is a deadly disease among the most common malignant tumors worldwide, and it has become an increasingly important public health problem in developing and low‐to‐middle income countries. This study aims to use the convolutional neural network (CNN) deep learning algorithms to develop an automated classification and detection model for oral cancer screening. Methods The study included 700 clinical oral photographs, collected retrospectively from the oral and maxillofacial center, which were divided into 350 images of oral squamous cell carcinoma and 350 images of normal oral mucosa. The classification and detection models were created by using DenseNet121 and faster R‐CNN, respectively. Four hundred and ninety images were randomly selected as training data. In addition, 70 and 140 images were assigned as validating and testing data, respectively. Results The classification accuracy of DenseNet121 model achieved a precision of 99%, a recall of 100%, an F1 score of 99%, a sensitivity of 98.75%, a specificity of 100%, and an area under the receiver operating characteristic curve of 99%. The detection accuracy of a faster R‐CNN model achieved a precision of 76.67%, a recall of 82.14%, an F1 score of 79.31%, and an area under the precision‐recall curve of 0.79. Conclusion The DenseNet121 and faster R‐CNN algorithm were proved to offer the acceptable potential for classification and detection of cancerous lesions in oral photographic images.

Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis

“…This analysis included 14 studies [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Table 1 presents the assessment of bias.…”

“…Since its introduction in 1991 [ 36 ], OCT has been developed to provide high-resolution images at a faster speed and has played an important role in the biomedical field. In an AI analysis study of OCT images published by Yang et al, it was reported that the sensitivity and specificity of oral cancer diagnosis was 98% or more [ 22 ]. In our study, OCT images were found to be the most accurate diagnostic test, with sensitivity of 94% in AI diagnosis compared to other image tools (sensitivity of autofluorescence and photographic images of 89% and 91%, respectively).…”

“… Forest plot of the diagnostic odds ratios for ( A ) screening only oral cancerous lesions [ 13 , 16 , 17 , 21 , 22 , 23 , 25 ] and ( B ) screening all premalignant mucosal lesions [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 ]. …”

“… Forest plots of ( A ) sensitivity, ( B ) specificity, and ( C ) negative predictive values for screening oral cancerous lesions [ 13 , 16 , 17 , 21 , 22 , 23 , 25 ]. …”

“…The Egger's test result (p > 0.05) also shows that the possibility of publication bias is low. Forest plot of the diagnostic odds ratios for (A) screening only oral cancerous lesions [13,16,17,[21][22][23]25] and (B) screening all premalignant mucosal lesions [13][14][15][16][17][18][19][20][21]23,24].…”