基于SVM和CNN的黑色素瘤皮肤癌检测

Q2 Computer Science
Sai Pranav Kothapalli, Panchumarthi Sri Hari Priya, Vempada Sagar Reddy, Botta Lahya, Prashanth Ragam
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引用次数: 0

摘要

在癌症检测和预防领域,当涉及到癌症预测时,医生和患者都面临着许多挑战。黑色素瘤皮肤癌是一种致命的皮肤癌,在世界各地有多种变体。传统的方法包括人工检查,然后对样品进行各种测试。这种耗时的工作和不准确的预测有时会危及患者的整体健康。本文阐述了利用传统图像处理技术和基于机器学习和深度学习的方法解决皮肤癌检测问题的两个方面。本文综述了目前的皮肤癌检测技术,权衡了这些技术的优点和缺点,并介绍了一些相关的癌症数据集。该方法主要关注黑色素瘤皮肤癌的检测及其早期阶段(普通痣和非典型痣)。所提出的方法采用颜色、纹理和形状特征的混合来从图像中获得区分属性。使用CNN(卷积神经网络)和SVM(支持向量机)算法对患者的皮肤癌类型进行识别,准确率分别达到92%和95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Melanoma Skin Cancer Detection using SVM and CNN
In the field of cancer detection and prevention, doctors and patients are facing numerous challenges when it comes to cancer prediction. Melanoma skin cancer is a deadly type of skin cancer with a multitude of variants spread across the world. Traditional methods involved manual inspection followed by various tests of samples. This time-consuming work and inaccurate predictions sometimes risk the overall health of the patient. The two aspects of solving skin cancer detection problems utilising both conventional image-processing techniques and methods based on machine learning and deep learning are elaborated in this article. It gives a review of current skin cancer detection techniques, weighs the benefits and drawbacks of those techniques, and introduces some relevant cancer datasets. The proposed method focuses mainly on Melanoma skin cancer detection and its previous stages (Common Nevus and Atypical Nevus). The methods being proposed employ a blend of colour, texture, and shape characteristics to derive distinguishing attributes from the images. Using CNN (convolutional neural networks) and SVM (support vector machine) algorithms to identify the type of skin cancer the patient is affected with and achieved an accuracy of 92% and 95% respectively.
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来源期刊
EAI Endorsed Transactions on Pervasive Health and Technology
EAI Endorsed Transactions on Pervasive Health and Technology Computer Science-Computer Science (miscellaneous)
CiteScore
3.50
自引率
0.00%
发文量
14
审稿时长
10 weeks
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