Skin Cancer Detection: A Review Using Machine Learning Techniques

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. The CNN model came up with an accuracy of 74% being the highest from the rest of the five models performance. The study underscores the potential of machine learning in enhancing early detection capabilities, thereby aiding medical professionals in more accurate diagnoses and timely intervention for improved patient outcomes. Continued research in this domain is essential for refining algorithms, incorporating more extensive datasets, and advancing the integration of AI into clinical practices for enhanced cancer diagnostics.