Oral squamous cell carcinoma (OSCC) is a common type of cancer of the oral cavity. Despite their great impact on mortality, sufficient screening techniques for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate recognition of OSCCs would lead to an improved curative result and a reduction in recurrence rates after surgical treatment. The introduction of image recognition technology into the doctor's diagnosis process can significantly improve cancer diagnosis, reduce individual differences, and effectively assist doctors in making the correct diagnosis of the disease. The objective of this study was to assess the precision and robustness of a deep learning-based method to automatically identify the extent of cancer on digitized oral images. We present a new method that employs different variants of convolutional neural network (CNN) for detecting cancer in oral cells. Our approach involves training the classifier on different images from the imageNet dataset and then independently validating on different cancer cells. The image is segmented using multiscale morphology methods to prepare for cell feature analysis and extraction. The method of morphological edge detection is used to more accurately extract the target, cell area, perimeter, and other multidimensional features followed by classification through CNN. For all five variants of CNN, namely, VGG16, VGG19, InceptionV3, InceptionResNetV2, and Xception, the train and value losses are less than 6%. Experimental results show that the method can be an effective tool for OSCC diagnosis.
Copyright © 2021 Hao Zhang et al.