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Identification of Melanoma through Dermoscopy Image using Learning Vector Quantization

机译:使用学习矢量量化通过Dermoscopy图像鉴定黑色素瘤

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Melanoma is one of the rare and malignant types of skin cancer.There are numerous ways to detect the melanoma,and one of them is through doctor diagnosis.A doctor can detect melanoma after a thorough medical check-up.If the patient has symptoms of melanoma,a biopsy will be carried out.This process requires a long time making it inconvenient.Therefore,an approach of the image processing system is necessary to assist the experts in diagnosing melanoma.The process consists of image input using the dermoscopy image,a pre-processing process of grey-scaling and median filtering,and feature extraction using Grey-Level Cooccurrence Matrix(GLCM).In the final step,a classification process will be performed using learning vector quantization.Based on the experimental test,the system generated an accuracy of 83.33% in identifying melanoma cancer.
机译:黑色素瘤是一种罕见和恶性的皮肤癌之一。有许多方法可以检测黑素瘤,其中一个是通过诊断诊断。在彻底的医学检查后,医生可以检测黑色素瘤。如果患者有症状 黑色素瘤,活检将进行。此过程需要很长时间,使其不方便。因此,需要一种方法来帮助专家诊断黑素瘤。该过程包括使用Dermicocy图像的图像输入,a 使用灰度Cooccurrence矩阵(GLCM)的灰度缩放和中值滤波的预处理过程。在最终步骤中,将使用学习矢量量化进行分类过程。基于实验测试,系统生成 鉴定黑素瘤癌症的准确性为83.33%。

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