Melanoma Skin Cancer Detection using SVM and CNN

被引：0

作者：

Kothapalli S.P. ^{[1
]}

Priya P.S.H. ^{[1
]}

Reddy V.S. ^{[1
]}

Lahya B. ^{[1
]}

Ragam P. ^{[1
]}

机构：

[1] School of Computer Science and Engineering, VIT-AP University, Inavolu Beside AP secretariat, Andhra Pradesh, Amaravati

来源：

EAI Endorsed Transactions on Pervasive Health and Technology | 2023年 / 9卷 / 01期

关键词：

Convolutional Neural Networks; Deep Learning; Image Processing; Machine Learning; Skin Cancer Detection; Support Vector Machine;

D O I：

10.4108/eetpht.9.4340

中图分类号：

学科分类号：

摘要：

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. © 2023 S. P. Kothapalli et al.

引用

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