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Digital Image Based Segmentation and Classification of Tongue Cancer Using CNN

机译:基于数字图像的舌癌分割和分类(基于CNN)

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摘要

Due to the change in life style after covid-19 there is demand for non-invasive contact less healthcare monitoring systems. In India oral cancer rate are increasing and becoming the community health issue with high mortality rate. Mortality rate can be reduced by identification of disease at initial stages. The major hindrance in disease identification is availability of dedicated hardware at remote and identification at initial stage. Research is going on to make the device portable and less costly using digital images. Also, research is going on to have hybrid algorithm which can segment smaller area abnormal area and classify reliably. This paper focuses on tongue cancer which is one of the types of oral cancer and presents comparison of hybrid algorithm using firefly and watershed transformation to segment smaller area using digital images which reduces cost of dedicated hardware required. 150 digital images are used which are available on internet or provided by cancer hospital for analysis and classification using CNN along with augmentation. 90.48 accuracy is achieved and desirable results are obtained using hybrid algorithm being used.
机译:由于covid-19之后生活方式的改变,对非侵入式非接触式医疗保健监测系统的需求。在印度,口腔癌发病率正在上升,并成为死亡率高的社区健康问题。通过在初始阶段识别疾病,可以降低死亡率。疾病识别的主要障碍是远程专用硬件的可用性和初始阶段的识别。目前正在进行研究,以使用数字图像使该设备便携且成本更低。此外,研究正在进行中,该算法可以分割较小区域的异常区域并可靠地进行分类。本文重点介绍口腔癌的一种类型舌癌,并比较了使用萤火虫变换的混合算法和流域变换,以使用数字图像分割较小的区域,从而降低所需专用硬件的成本。使用了 150 张数字图像,这些图像可在互联网上获得或由癌症医院提供,以便使用 CNN 和增强进行分析和分类。使用混合算法实现了90.48%的准确率,并获得了理想的结果。

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