Automated Multiclass Dermatological Diagnosis Prediction using Improved Mobile-Net Model

Skin disorders are prevalent and difficult medical conditions that impact millions of individuals globally. For these illnesses to be effectively treated and potential complications to be avoided, a prompt and precise diagnosis is essential. Dermatologists and other medical professionals may now diagnose skin diseases and abnormalities more accurately thanks to the development of several automated skin detection technologies. The application of Deep Learning (DL) technology to the diagnosis and categorization of skin disorders has showed considerable potential. These techniques automatically evaluate and categorize skin photos according to patterns, textures, and characteristics using artificial neural networks, more especially convolutional neural networks (CNNs). In this paper, we examine the prediction of automatic skin detection using Mobile-Net model and HAM10000 dataset. This research utilizes the ReduceLROnPlateau function to reduce the learning rate of proposed Mobile-Net model. From this experimental analysis, the proposed Mobile-Net model provides a top 2 accuracy 0.927, top3 accuracy 0.9648 and less validation loss compared with existing mobilenet model.