| dc.description.abstract | Abstract - Skin cancer is a serious health concern, and early detection is crucial for effective treatment. Machine learning algorithms have shown promise in detecting skin cancer, but there is still much to be explored in terms of their effectiveness and efficiency. This paper presents a comparative analysis of different machine learning algorithms for skin cancer detection, including Support Vector Machines, VGG16, VGG19, Inception, Xception , and Convolutional Neural Networks. The study uses a dataset of 30,000 skin images, from which 21000 images are provided as training data and the rest 9000 are put in testing dataset. In the case of skin cancer detection, machine learning can be used to analyze images of skin lesions and identify those that are likely to be cancerous. This can help doctors to make more accurate diagnoses and provide earlier treatment. The results show that the neural network algorithm outperforms the other algorithms in terms of accuracy and speed. | en_US |
