A Survey on Machine Learning Techniques for Skin Disease Detection and Product Recommendation

Skin-related problems such as acne, dry skin, oily skin, normal skin, and skin sensitivity affect a large portion of the population and often require timely identification for proper care.With the increasing availability of digital images and advancements in artificial intelligence, machine learning and deep learning techniques have gained significant attention in automated skin condition analysis.This paper presents a survey of existing machine learning and deep learning approaches used for skin disease and skin type detection, with a focus on imagebased classification methods.Various techniques including traditional machine learning models and transfer learning approaches such as Mobile Net and VGG are reviewed and compared based on their performance, dataset usage, and limitations.The study highlights that while many existing systems focus on complex skin diseases, relatively less attention has been given to common skin conditions that affect daily life.Based on the survey, a conceptual framework for a skin condition detection system using a small custom dataset is proposed.The proposed system aims to classify common skin conditions and provide basic rule-based skincare product recommendations suitable for general users.This survey helps in understanding current research trends and provides a foundation for developing cost-effective and user-friendly skin analysis systems.Index Terms-Skin Disease Detection, Machine Learning, Deep Learning, Transfer Learning, Product Recommendation.Type detection and proposes a conceptual framework for a skin condition detection system with basic skincare product recommendation for general users.