Eczema Detection in Dermatological Imaging: From Traditional Methods to Deep Learning and Mobile Deployment

Abstract

Atopic dermatitis, commonly referred to as eczema, is a chronic inflammatory skin condition characterized by recurrent redness, itching, and irritation. Its clinical significance lies in its long-term discomfort and significant impact on an individual’s quality of life. Traditionally, the diagnosis of eczema has relied on visual examination by a dermatologist, patient interviews, and manual assessment using a clinical scoring system. Although effective, these methods are limited by interobserver variability, inconsistencies in clinical expertise, and scalability challenges. This is particularly true in settings with limited dermatology resources. These limitations underscore the need for more objective, accessible, and technology-driven diagnostic approaches. This paper reviews the evolution of eczema detection, tracing the transition from conventional clinical assessment to modern algorithm-assisted methods. It highlights advances in medical imaging, feature extraction techniques, and sophisticated deep learning models, with particular emphasis on their suitability for mobile and real-time use. Treatment strategies and non-dermatological conditions are intentionally excluded to maintain the focus on diagnostic innovation. Overall, this review provides a comprehensive analysis of historical practices, recent technological advances, existing challenges, and emerging opportunities in automated eczema detection.