Using an Improved Visual Geometry Group Neural Network for Skin Cancer Detection

Abstract

Over time, deep learning has become more accurate and efficient at dealing with more complex risks. However, training deep neural networks remains a daunting problem and challenges remain, possibly due to several issues, including relevance, generalizability, and training time. Producing specific processors to manipulate artificial networks and reduce task-specific network learning time is natural, but the challenge of fit and generalizability remains undisputed. These networks have been used to detect and diagnose skin cancer, one of the most dangerous types of cancer caused by DNA damage. Automatic recognition of skin cancer is important to help doctors detect the disease in its early stages. In this research, the proposed vgg16 method was used to classify benign and malignant medical images. Image processing was used, where Gaussian noise and salt and pepper noise were added to increase the data, and filters for the arithmetic mean and median were added for each noise. The network was implemented using the Keras interface. Appling suggest model shown that the height accuracy 0.86 for train and accuracy 0.92 for test