Abstract: Scalp disorders, affecting millions worldwide, significantly impact both physical and mental health. Deep learning has emerged as a promising tool for automated diagnosis, but ensuring model transparency and reliability is crucial. This review explores the integration of explainable AI (XAI) techniques to enhance the interpretability of deep learning models in scalp disorder diagnosis. We analyzed recent studies employing deep learning models to classify scalp disorders from image data and used XAI methods to understand the models' decision-making processes and identify potential biases. While deep learning has shown promising results, challenges such as data quality and model interpretability persist. Future research should focus on expanding the capabilities of deep learning models for real-time detection and severity prediction, while addressing limitations in data diversity and ensuring the generalizability of models across different populations. The integration of XAI techniques is essential for fostering trust in AI-powered scalp disease diagnosis and facilitating their widespread adoption in clinical practice.

Vinh Quang Tran and Haewon Byeon, “Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151128

@article{Tran2024,
title = {Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151128},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151128},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Vinh Quang Tran and Haewon Byeon}
}