Abstract : Oral cancer is a major global health issue accounting for 177,384 deaths in 2018 and it is most prevalent in low- and middle-income countries. Enabling automation in the identification of potentially malignant and malignant lesions in the oral cavity would potentially lead to low-cost and early diagnosis of the disease. Building a large library of well annotated oral lesions is key. As part of the MoMoSA (MobileMouth Screening Anywhere) project, images are currently in the process of being gathered from clinical experts from across the world, who have been provided with an annotation tool to produce rich labels. A novel strategy to combine bounding box annotations from multiple clinicians is provided in this project. Further to this, deep neutral networks were used to build automated systems, in which complex patterns were derived for tackling this difficult task. Using the initial data gathered in this study, two deep learning-based computer vision approaches were assessed for the automated detection and classification with ResNet-101 and object detection with the Faster R-CNN. We create a methodology to extract features from image and implement genetic algorithm and apriori algorithm for association mining to get accuracy more in results.
Key Words: Malignant lesion, Machine Learning
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DOI:
10.17148/IJARCCE.2021.106121
