May 2026 in “International Journal of Scientific Research in Science and Technology” This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.
This study introduced a deep learning framework combining multiple convolutional neural networks to detect scalp and hair disorders and classify hair fall stages, reporting higher precision and robustness in detection and classification compared to individual CNN models.
This study found that GoogLeNet outperformed other CNN models in accurately identifying the type of folliculitis.
January 2026 in “JDDG Journal der Deutschen Dermatologischen Gesellschaft” This study developed a deep-learning model that accurately diagnosed alopecia areata with an accuracy of 88.92% and distinguished its activity levels with an accuracy of 83.33%, highlighting the potential for artificial intelligence in improving the diagnosis and treatment of this autoimmune hair loss condition.