Revolutionizing Hair Fall Analysis: The Advanced Precipitation U-Net Model
DOI:
https://doi.org/10.63682/jns.v14i32S.8104Keywords:
Hair fall prediction, Machine learning, Deep learning, U-Net, Comparative analysisAbstract
Around the world, long and thick hair is considered a sign of youth, while thick hair in humans is a symbol of youth and vitality. Approximately 80 trillion people suffer from hair fall due to aging, stress, medication, or genetic makeup globally. Hair and scalp-related diseases often go unnoticed in the beginning. Sometimes, a patient cannot differentiate between hair fall and regular hair fall. Diagnosis of hair-related diseases is time-consuming as it requires professional dermatologists to perform visual and medical tests. Machine learning and deep learning studies become a complex overlap between hair loss and psychological issues, making accurate detection difficult. Because of this, the overall diagnosis gets delayed, further increasing the severity of the disease. This study leverages the image-processing capability such as neural network-based applications used in various fields, especially healthcare and health informatics, to predict malignant diseases such as cancer and tumors. This study uses the U-Net to 92% accurately segment even small or thin structures, such as individual hair strands, which is a major challenge in hair fall detection. By producing precise segmentation masks, U-Net helps doctors and researchers identify areas of hair fall more reliably and early, overcoming the difficulties of gradual thinning and subtle changes that are hard to spot with the naked eye.
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