Melanoma Skin Cancer Detection Using Support Vector Machines and Convolutional Neural Networks

Christopher Ebuka Ojukwu¹

Section:Research Paper, Product Type: Journal-Paper
Vol.9 , Issue.6 , pp.9-12, Dec-2021

Online published on Dec 31, 2021

Copyright © Christopher Ebuka Ojukwu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Speed remains an important factor in the detection of skin cancer. Like any other medical condition, it worsens with time. In this paper, two machine learning models are built using support vector machines and convolutional neural networks to detect malignant melanoma skin cancer. An open-source dataset of seven hundred malignant and benign skin cancer images was downloaded from the Kaggle website. Eighty percent of the dataset was used for training the models while twenty percent of the dataset was used for testing the models. The models were executed in Jupyter Notebook while using pandas, NumPy, and TensorFlow. The goal of this paper is to train two models using the same dataset, hardware specifications, and software environment to ascertain the individual accuracies of the models in skin cancer detection and foster a fair comparison.

Key-Words / Index Term :
Machine Learning; Convolutional Neural Networks; Support Vector Machines; Skin Cancer Detection

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