Skin Cancer Recognition and Detection Using Machine Learning Algorithm

A. Jenitha, G. Amrutha, K. Kishore, K. Rohan, S. Sagar
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引用次数: 1

Abstract

In this paper, we concentrate on the identification of skin cancer. The skin images are taken from a medical database which is a pre-processed image, which is given as input for different machine learning algorithm. The algorithm used is KNN classifier, SVM classifier, and CNN model. where these classifiers will classify whether a given image is cancerous or non-cancerous image. In case of the KNN and SVM the output is 80%, hence in CNN model substantial improvement in accuracy of cancer detection is obtained & it can classify the cancerous & Non-cancerous images efficiently. The process was conducted for test data, training data and validation data using different-images. The training dataset was trained with 100 epochs. The process obtained the accuracy of 97% in training result. in testing result obtained is 95% of accuracy and 96% for validation testing.
基于机器学习算法的皮肤癌识别与检测
在本文中,我们集中在皮肤癌的识别。皮肤图像取自医学数据库,该数据库是经过预处理的图像,作为不同机器学习算法的输入。使用的算法有KNN分类器、SVM分类器和CNN模型。这些分类器将对给定图像是癌变图像还是非癌变图像进行分类。在KNN和SVM的情况下,输出为80%,因此在CNN模型中,癌症检测的准确率得到了很大的提高,可以有效地对癌和非癌图像进行分类。该过程对不同图像的测试数据、训练数据和验证数据进行了处理。训练数据集用100个epoch进行训练。该过程在训练结果中获得了97%的准确率。测试结果准确率为95%,验证测试准确率为96%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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