Machine Learning in the Diagnosis and Classification of Erythemato Squamous Disorders: Current Trends and Future Directions

Psoriasis, a condition called seborrheic dermatitis, and lichen planus are among the various dermatological illnesses known as erythemato-squamous diseases, which are distinguished by erythematous (red) and scaly signs.Accurately diagnosing those diseases is difficult since their symptoms frequently overlap.Conventional diagnostic techniques, such as skin biopsies and clinical examinations, can rarely produce accurate distinction and are frequently time-consuming.New developments in machine learning (ML) methods, including data mining, image processing, and supervised classifiers, have shown great promise in improving the precision and effectiveness of diagnosis.This review of the literature looks at many machine learning (ML)-based methods for erythemato-squamous illness diagnosis, with an emphasis on feature extraction, classification accuracy, and clinical applicability.The review emphasizes how ML algorithms might help patients with skin disorders by streamlining diagnostic processes, enhancing early diagnosis, and enabling individualized treatment options.