Early detection of tongue cancer using a convolutional neural network and evaluation of the effectiveness of EfficientNet

Objectives: Early detection of oral cancer is critical because the survival rate is reported to be 90%, whereas 45.5% for advanced oral cancer occur with neck metastasis. This study assessed the detection potential of oral tongue cancer and precancerous lesions using a convolutional neural network (CNN) and evaluated the effectiveness of EfficientNet in a situation with limited datasets. Materials: and Methods 1,810 tongue images organized into four categories (malignant tumors, precancerous lesions, benign or inflammatory lesions, and normal) were used for model training. Data augmentation, transfer learning, and fine-tuning have been used to overcome the problems associated with limited datasets. Also, the weight balancing method was introduced to mitigate class imbalance by assigning different weights to each class. VGG16, Inception-ResNet-V2, and EfficientNet models were used and compared to each other. Results: This study evaluated the possibility of detecting tongue cancer and precancerous lesions using CNN. The final model achieved an accuracy of 0.9167, a precision of 0.9212, a recall of 0.9167, and an F 2 score of 0.9176 with the test dataset. Conclusions: Our proposed model can detect potential oral cancer and precancerous lesions. EfficientNet was effective through data augmentation and weight balancing in detecting the tongue lesions with a limited dataset. The results of this study may help patients and general practitioners in tongue lesion diagnosis. Clinical relevance In many cases, tongue cancer is initially confused with glossitis, even by general practitioners. Thus, screening of tongue cancer/precancerous lesions through an artificial neural network will help detect tongue cancer early.