AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer

Abstract

Artificial intelligence (AI) applications in oncology have been developed rapidly with reported successes in recent years. This work aims to evaluate the performance of deep convolutional neural network (CNN) algorithms for the classification and detection of oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) in oral photographic images. A dataset comprising 980 oral photographic images was divided into 365 images of OSCC, 315 images of OPMDs and 300 images of non-pathological images. Multiclass image classification models were created by using DenseNet-169, ResNet-101, SqueezeNet and Swin-S. Multiclass object detection models were fabricated by using faster R-CNN, YOLOv5, RetinaNet and CenterNet2. The AUC of multiclass image classification of the best CNN models, DenseNet-196, was 1.00 and 0.98 on OSCC and OPMDs, respectively. The AUC of the best multiclass CNN-base object detection models, Faster R-CNN, was 0.88 and 0.64 on OSCC and OPMDs, respectively. In comparison, DenseNet-196 yielded the best multiclass image classification performance with AUC of 1.00 and 0.98 on OSCC and OPMD, respectively. These values were inline with the performance of experts and superior to those of general practictioners (GPs). In conclusion, CNN-based models have potential for the identification of OSCC and OPMDs in oral photographic images and are expected to be a diagnostic tool to assist GPs for the early detection of oral cancer.

A Novel Method for the Detection of Oral Cancers: Deep Learning

“…Integration of AI-supported apps into mobile phones can effectively facilitate oral cancer screening in rural areas with limited access to healthcare services [24]. A previous study evaluated algorithms for the diagnosis and classi cation of oral cancers using intraoral photographs, and found that algorithms outperformed general practitioners and exhibited a performance similar to that of specialist physicians [25].…”

Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review

“…Previous studies have developed systems using simple photographs, 27–29 smartphone use, 23–25,30,31 and web application 32,34 . For instance, Warin and collaborators reported a pipeline composed of segmentation and classification CNNs capable to detect and classify OPMDs in oral photographs.…”

“…Noteworthy, they concluded that CNN‐based models have great potential for this application 27 . Warin and collaborators also reported a Multiclass object detection model for early detection of OSCC, and demonstrated that DenseNet‐196 had a great performance in OSCC (AUC of 0.98) and OPMDs (AUC of 1.00) 28 …”

“…[23][24][25] Particularly, different clinical applications using ML techniques have also been explored for OPMDs, evidencing the search for efficient methods of analysis capable of predicting the patient's follow-up. 26 Previous studies have developed systems using simple photographs, [27][28][29] smartphone use, [23][24][25]30,31 and web application. 32,34 For instance, Warin and collaborators reported a pipeline composed of segmentation and classification CNNs capable to detect and classify OPMDs in oral photographs.…”

“…27 Warin and collaborators also reported a Multiclass object detection model for early detection of OSCC, and demonstrated that DenseNet-196 had a great performance in OSCC (AUC of 0.98) and OPMDs (AUC of 1.00). 28 Spyridonos et al 29 investigated solar cheilosis aiming to determine a probabilistic model for its improved detection in real clinical environments using fractal features and type-P Fourier descriptors.…”