Melanoma Skin Cancer Detection using SVM and CNN


  • Sai Pranav Kothapalli Vellore Institute of Technology University image/svg+xml
  • Panchumarthi Sri Hari Priya Vellore Institute of Technology University image/svg+xml
  • Vempada Sagar Reddy Vellore Institute of Technology University image/svg+xml
  • Botta Lahya Vellore Institute of Technology University image/svg+xml
  • Prashanth Ragam Vellore Institute of Technology University image/svg+xml



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


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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How to Cite

Kothapalli SP, Hari Priya PS, Sagar Reddy V, Lahya B, Ragam P. Melanoma Skin Cancer Detection using SVM and CNN. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Nov. 9 [cited 2023 Dec. 10];9. Available from: