# Performance of Automated Oral Cancer Screening Algorithm in Tobacco Users vs. Non-Tobacco Users
## 3 Results  
All 318 subjects enrolled in the study participated for its entire duration and in full compliance with the IRB protocol. Table 2 shows a breakdown of subject demographics, of whom 247 self-identified as non-smokers, and 71 as smokers. About 1099 pairs (AFI and pWLI) of intraoral images, or a total of 2198 individual images were collected. These image pairs were categorized as follows: Category (A): \"no increased risk\" contained 786 image pairs and category (B): \"increased risk\" contained 313 image pairs. The smoking group contained 119 \"no increased risk\" image pairs and 126 \"increased risk\" image pairs. The non-smoking group contained 667 \"no increased risk\" image pairs and 187 \"increased risk\" image pairs. All of these categorizations were based on the standard-of-care specialist screening outcome. All of the algorithm training was completed using a VGG19 model pre-trained with ImageNet. The experiments were implemented in the python platform using Tensorflow and Keras tools. For each training pathway, the dataset was randomly split into training and validation to perform fourfold cross-validation, and the results from the folds were averaged. The batch size was 32, the learning rate was 0.001, the epoch number was 300, and Adam optimizer was used for each experiment. Since the non-smoking group is imbalanced, we randomly over sampled (ROS) the minority class identified as carrying \"increased risk\" using data augmentation, and randomly under sampled (RUS) the majority class that was identified as having \"no increased risk\". After ROS and RUS, the dataset for training and cross-validation contained 374 \"no increased risk\" image pairs and 374 \"increased risk\" image pairs. Our previous study has shown the class bias induced by an imbalanced OC image dataset can be reduced using these data-level approaches [52]. For dual-modal image (white light and autofluorescence images) data input, we used an image fusion method reported in a previous study [34], which created a new three-channel image dataset from autofluorescence and white light images and this was then fed into the network for training. A total of 1099 pairs (pWLI and AFI) of intraoral images were collected from the 318 study subjects (Figures 4 and 5). Figure 4 shows image pairs of typical erythemat lesions in the anterior tonsillar pillar region obtained from a smoker and a non-smoker. In the pWLI, the smoker's tissues appear hyperkeratinised, thickened, and fibrous (Figure 4(a)), which is typical of smoking-related changes in the oral soft tissues. These changes are also clearly visible in the AFI of the smoker (Figure 4(b)). Because thickening and whitening of the soft tissues can also be an indicator of OPML or OC, there is the potential for a confluence of smoking-related and neoplasia-related mucosal changes in these individuals, as shown in these images. In contrast, the same type of lesion in the non-smoker appears more moist and considerably less fibrosed, less white, and less thick in the pWLI and the AF images (Figure 4 ii(a) and Figure 4 ii(b)). Figure 5 shows image pairs of papillary hyperplasia from a smoker ((Figure 5i)), and a non-smoker ((Figure 5 ii)). The thick, red, dry looking fibrosed surface layer in the smoker's gingival tissues extends across the entire visible gingival surface, with a quite uniform white color, coupled with increased bulk across the entire spread of the tissues, from the gingival margin extending up to the mucosa \begin{table}
\begin{tabular}{c c c} \hline \hline
**Identifying Gender** & **Race/Ethnicity** & **Age** \\ \hline
137 Female & 124 White/Hispanic & Range: 37–89 years of age \\ \hline
181 Male & 91 White/Non-Hispanic & Mean: 54 years of age \\ \hline  & 66 Asian & Median: 41 years of age \\ \hline  & 19 more than one race & \\ \hline  & 14 African American & \\ \hline  & 4 Pacific Islander & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Subject demographics. (Figure 5i(a)).