| dc.description.abstract | The rising prevalence of facial dermatoses, including acne and their variants, ne cessitates the development of effective diagnosis and classification techniques. This
systematic review evaluates optimal technologies for detecting pimples and classifying
facial skin diseases by analysing diverse image processing and machine learning method ologies. The review examines research employing approaches such as Convolutional
Neural Networks (CNNs), texture feature extraction, and hybrid strategies that integrate
multiple algorithms for detection with high precision. It critically assesses the strengths
and limitations of existing technologies in terms of their performance and clinical
applicability. Findings highlight significant advancement in automated skin assessment,
yet underscore persistent challenges related to dataset diversity, model generalizability,
and integration into practical clinical applications. The review emphasizes the necessity
of larger, more diverse datasets and the adoption of advanced machine learning
techniques to enhance detection performance. Future research directions are proposed
to address these gaps, aiming to develop superior tools for dermatologists and patients.
These advancements are envisioned to facilitate early diagnosis and treatment of facial
skin disorders, ultimately improving patient outcomes. | en_US |
