Deep Learning in Dermatology: A Review on Skin Type Classification and Disease Detection

Deep Learning in Dermatology: A Review on Skin Type Classification and Disease Detection

T. Anoushkaa1, K V Dikshitha2, P. Lakshmi Pranitha3, Prof. K. Vaidehi4

1, 2, 3Student, Department of ADCE, Stanley College of Engineering and Technology for Women, Hyderabad

4HOD, Department of ADCE, Stanley College of Engineering and Technology for Women, Hyderabad

---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - Advancements in technology have significantly improved product recommendations, enhancing customer satisfaction and trust. Building upon this progress, the present paper explores a Deep Learning-based Skin Type and Skin Disease Analysis system designed to identify an individual's skin condition and provide personalized recommendations for skincare and diet. This system employs state-of-the-art models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which are chosen for their scalability in terms of network width, depth and resolution. By integrating advanced deep learning techniques with image analysis, this project aims to provide users with reliable, data-driven insights into their skin health, fostering informed skincare and lifestyle choices.

Key Words: Compound Annual Growth rate (CAGR), Deep Learning, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN)