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Artificial Intelligence Based Skin Classification Using GMM

机译:基于人工智能的皮肤分类使用GMM

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This study describes the usage of neural community based on the texture evaluation of pores and skin a variety of similarities in their signs, inclusive of Measles (rubella), German measles (rubella), and Chickenpox etc. In fashionable, these illnesses have similarities in sample of infection and symptoms along with redness and rash. Various skin problems have similar symptoms. For example, in German measles (rubella), Chicken pox and Measles (rubella) a similarity can be observed in skin rashes and redness. The prognosis of skin problems take a long time as the patient's previous medical records, physical examination report and the respective laboratory diagnostic reports have to be studied. The recognition and diagnosis get tough due to the complexity involved. Subsequently, a computer aided analysis and recognition gadget would be handy in such cases. Computer algorithm steps include image processing, picture characteristic extraction and categorize facts with the help of a classifier with Artificial Neural Network (ANN). The ANN can analyze the patterns of symptoms of a particular disease and present faster prognosis and reputation than a human doctor. For this reason, the patients can undergo the treatment for the pores and skin problems based totally on the symptoms detected.
机译:本研究描述了神经界的使用基于毛孔和皮肤的纹理评估,其迹象中的各种相似性,包括麻疹(风疹),德国麻疹(风疹)和水痘等时尚,这些疾病具有相似之处感染和症状样本以及发红和皮疹。各种皮肤问题具有类似的症状。例如,在德国麻疹(风疹)中,鸡痘和麻疹(风疹)可以在皮疹和发红中观察相似性。皮肤问题的预后需要很长时间作为患者以前的医疗记录,体检报告和各自的实验室诊断报告。由于所涉及的复杂性,识别和诊断变得艰难。随后,在这种情况下,计算机辅助分析和识别小工具将是方便的。计算机算法步骤包括借助人工神经网络(ANN)的分类器的图像处理,图片特征提取和分类事实。 ANN可以分析特定疾病的症状模式,并提出比人类医生更快的预后和声誉。因此,患者可以完全基于检测到的症状进行孔隙和皮肤问题的治疗。

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