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@article{155723,
        author = {Sasikumar and C.Anitha},
        title = {Oral Cancer Detection by CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {1535-1543},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155723},
        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. In this paper we developed neural networks based oral cancer detection using different image processing techniques. For that we are using Convolutional Neural Network for the automated detection of oral lesions for the early detection of oral cancer. Oral cancer is a quite common global health issue. Early diagnosis of cancerous and potentially malignant disorders in the oral cavity would significantly increase the survival rate of oral cancer. Previously reported smartphone-based images detection methods for oral cancer mainly focus on demonstrating the effectiveness of their methodology, yet it still lacks systematic study on how to improve the diagnosis accuracy on oral disease using hand-held smartphone photographic images. We conducted a retrospective study. First, a simple yet effective centered rule image-capturing approach was proposed for collecting oral cavity images. Then, based on this method, a medium-sized oral dataset with five categories of diseases was created, and a resampling method was presented to alleviate the effect of image variability from hand-held smartphone cameras. Finally, a recent deep learning network (HRNet) was introduced to evaluate the performance of our method for oral cancer detection.},
        keywords = {Automated object tracking drone, Object detection using OpenCV, Object detection, Face recognition.},
        month = {},
        }