Skin Disease Detection Employing Transfer Learning Approach- A fine-tune VGG-19

Abstract

Your skin may become damaged by skin diseases and conditions. These illnesses can cause skin changes such as rashes, inflammation, itching, and other skin changes. While some skin conditions may run in families, others may result from a person's way of life. Pills, creams, ointments, and changes in lifestyle are all potential treatments for skin conditions A large amount of data for model training and improvements in model designs that provide stronger simplifications have led to a rapid advancement of deep learning algorithms for applications involving computer vision. Undesired skin disease regions are eliminated, quality is raised, and the disease is tinted by discarding artifacts, decrease noise, and improving the image. Three augmentation techniques have led to an increase in the number of skin disease images. Several CNN architectures, including VGG16, VGG19, MobileNet, MobileNetV2, and InceptionV3, looked at the augmentation dataset. VGG-19 offers the highest level of accuracy in this case. Following the segmentation of the dermoscopic images, the affected skin cells' features are extracted using a feature extraction technique. Using a convolutional neural network classifier, which is based on deep learning, the extracted features are stratified. The best outcomes were obtained using the hyper-tuned VGG19, which had test and validation accuracy of 99.21% and 99.25%, including both.