Estimating hair density with XGBoost.

Objectives: Hair density estimation is crucial in dermatology and trichology; however, manual counting is time-consuming and error-prone. Although automated approaches have been developed using image processing, neural networks, and deep learning, creating a robust and widely applicable method remains challenging. This study explored the use of XGBoost to estimate hair density with the aim of developing a more accurate and versatile approach.

Methods: The study utilized 895 scalp images to extract features and developed an XGBoost model for hair density estimation using 745 images to train the model and testing its performance on 150 images to evaluate the accuracy, error rate, and scatter plot.

Results: The XGBoost model outperformed previous methods, achieving 89.5% accuracy on the training set and 95.3% accuracy on the test set. This surpassed the results of Kim et al. (52.4%), Urban et al. (79.6%), and Sacha et al. (88.2%) for the test set.

Conclusion: The XGBoost algorithm proved to be effective for automated hair density estimation, achieving an accuracy of 95.3% on the test set. This approach, which focusses on scalp coverage and erosion features, can streamline and improve the objectivity of clinical hair analysis.