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

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.

Automated summary

This study successfully developed an automated classification and detection model for oral cancer screening using CNN deep learning algorithms, demonstrating the acceptable potential of DenseNet121 and faster R-CNN algorithms in the classification and detection of cancerous lesions in oral photographic images.