 on Tesla T4 graphics processing unit (GPU). With TTA applied, YOLOv5l achieved 0.953 on AP50 although this comes at a cost of slight reduction in AP since TTA generally works to increase recall at a cost of reduced precision. As expected, the model performance improved with bigger models, with the exception of YOLOv5x, which was prone to overfitting during training and achieved a lower AP compared to YOLOv5l on the test set. This could be due to the small size of our dataset, which is better suited to less complex models on this task. We further showed that the ensemble of two smaller models such as YOLOv5s and YOLOv5m can achieve almost the same AP as YOLOv5l but at a higher inference speed. With a very high inference speed and low computational cost, YOLOv5 offers great potential for deployment in a real-world application.
Different types of CNN models, including an ensemble model of DenseNet-161 and ResNet-152, were evaluated for the multi-class classification of oral lesions based on the risk of malignant transformation. Among all models, EfficientNet-b4 and Inception-v4 achieved the highest F 1 -score of 0.855 on the test set. The larger input dimensions and the advanced architectures of these models were likely to have provided performance gains over the other models that were evaluated for the classification task. We further analysed the results of EfficientNet-b4, which outperformed other models in terms of model complexity, computational cost, and accuracy combined. As shown in Table 7, the OPMD class had the lowest precision, with four benign and three carcinoma lesions misclassified as OPMD. Misclassification of carcinoma lesions as OPMD may not pose a significant risk since both types of lesions should be referred to a medical professional immediately. On the other hand, misclassification of benign lesions as OPMD may lead to increased referrals to oral cancer specialists and present an additional burden on the clinical staff. Nevertheless, the recall for OPMD was relatively high, which is encouraging since the detection of OPMD is of great importance for oral cancer screening.
The scope of the studies in the related literature have mostly been limited to certain types of oral lesions, such as mouth sores [17,18] or tongue lesions [19], which represent only a small fraction of the oral lesions. In a more recent study by Welikala et al., the authors investigated the feasibility of deep learning methods for detection and classification of oral lesions based on referral decisions using a more comprehensive dataset [20]. Although the results were encouraging, particularly for the binary classification of oral lesions as "nonreferral" or "referral", the models did not achieve good performance on the detection and multi-class classification of oral lesions. The classifiers were trained with the whole images of the oral cavity, instead of the target lesion areas, which may have impacted the accuracy of the models. Recently, Fu et al. proposed to use cascaded deep networks to detect OSCC from photographic images and compared the performance of the model with that of human readers [21]. The authors used a pretrained detector network as a pre-processing step to crop the lesion area and a classifier network to classify the candidate patches as oral cancer or normal mucosa. While the proposed model was shown to perform on par with human experts in identifying OSCC lesions, the study failed to investigate the model's capacity for differentiating between OSCC and non-OSCC oral diseases or between non-OSCC oral diseases and normal oral mucosa. In this work, we demonstrated the feasibility of classifying oral lesions into three categories based on their risk of progression into oral cancer, with reasonable precision and recall across all classes despite the size of our dataset and the variability of images. Unlike previous work, we utilized more recent and advanced architectures for classification, such as Inception-v4 and EfficientNet-b4, which provided significant improvements in evaluated metrics. As the models are trained with more images representing all the clinical variations of oral lesions, the model performance is expected to further improve significantly.
Finally, we proposed an end-to-end, two-stage model for identifying oral lesions by combining object detection and classification tasks in a serial manner. In our proposed pipeline, YOLOv5l is used for detecting lesion regions from the whole image and EfficientNet-b4 is used for classifying the detected lesion region into three categories. The selected networks achieved overall good performance in terms of both accuracy and inference time, which makes them highly suitable for deployment in a real-time application. As illustrated in Figure 7, the proposed model offers great potential for automated identification of various oral lesion types and paves the way for a low-cost, non-invasive, and easy-to-use oral cancer screening tool. It can be deployed as a mobile application for self-screening of the oral cavity or to support the oral cancer screening process at primary care centres, such as dental practices. Moreover, it could be used for remote follow-up of patients who have undergone surgery or received treatment for any disease recurrence.
the model with that of human readers [21]. The authors used a pretrained detector network as a pre-processing step to crop the lesion area and a classifier network to classify the candidate patches as oral cancer or normal mucosa. While the proposed model was shown to perform on par with human experts in identifying OSCC lesions, the study failed to investigate the model's capacity for differentiating between OSCC and non-OSCC oral diseases or between non-OSCC oral diseases and normal oral mucosa. In this work, we demonstrated the feasibility of classifying oral lesions into three categories based on their risk of progression into oral cancer, with reasonable precision and recall across all classes despite the size of our dataset and the variability of images. Unlike previous work, we utilized more recent and advanced architectures for classification, such as Inception-v4 and EfficientNet-b4, which provided significant improvements in evaluated metrics. As the models are trained with more images representing all the clinical variations of oral lesions, the model performance is expected to further improve significantly.
Finally, we proposed an end-to-end, two-stage model for identifying oral lesions by combining object detection and classification tasks in a serial manner. In our proposed pipeline, YOLOv5l is used for detecting lesion regions from the whole image and Effi-cientNet-b4 is used for classifying the detected lesion region into three categories. The selected networks achieved overall good performance in terms of both accuracy and inference time, which makes them highly suitable for deployment in a real-time application. As illustrated in Figure 7, the proposed model offers great potential for automated identification of various oral lesion types and paves the way for a low-cost, non-invasive, and easy-to-use oral cancer screening tool. It can be deployed as a mobile application for selfscreening of the oral cavity or to support the oral cancer screening process at primary care centres, such as dental practices. Moreover, it could be used for remote follow-up of patients who have undergone surgery or received treatment for any disease recurrence. . Proposed two-stage pipeline for oral cancer screening. It includes the detection of oral lesions from photographic images in the first stage and the classification of the detection region in the second stage using deep learning algorithms. YOLOv5l and EfficientNet-b4 models were selected for the pipeline as they achieved overall good performance in terms of both accuracy and inference time.

Conclusions
This study presents the potential applications of deep learning algorithms for segmentation, detection, and multi-class classification of oral lesions as benign, OPMD, or carcinoma. Based on the results obtained, a two-stage deep learning pipeline is proposed for oral cancer screening which enables automated detection and classification of various oral lesion types in real time with low computational costs. We envisage that the proposed model paves the way for a low-cost, non-invasive, and easy-to-use tool that can support screening processes and improve detection of OPMD. A larger dataset with more examples of challenging lesion types can bring significant gains to all of the models; therefore, it will be the focus of future studies.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/10.3390/cancers13112766/s1, Text S1: Semantic Segmentation: U-Net Experiments, Table S1: Backbones implemented with U-Net, number of model parameters (in million) and selected hyperparameters such as batch size and learning rate are shown for each model, Figure S1: Training plots showing dice loss and dice score on the validation set during training of the U-Net models with different backbones, Text S2: Instance Segmentation: Mask R-CNN Experiments, Figure S2: The learning curves for the Mask R-CNN models showing box and mask AP results on the validation set over 10,000 iterations, Text S3: Object Detection: YOLOv5 Experiments, Figure S3: The learning curves for the YOLOv5 models showing AP (left) and AP50 (right) on the validation set over 80 epochs, Text S4: Classification Experiments, Table S2: Selected hyperparameters for the classification experiments.Jaskirat Singh Sidhu went to jail Friday, 350 days after he poked his head through an opening in his upturned semi and got swallowed in chaos.
Twenty-nine families, and Sidhu himself, will wrestle forever with how this happened. There’s no definitive answer; only the mental picture.
Sidhu’s semi, traveling 86 to 96 km/h, intersected with the Humboldt Broncos’ bus at a highway crossing on April 6, 2018, killing 16 people and injuring 13 more. His truck left no tire marks. No brake was applied.
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