A Non Deep Learning based Method for Detection of Alopecia Areta and Segmentation of Scalp and Hair regions.

Hair and scalp-related diseases often go unnoticed in the initial stages and patients sometimes cannot differentiate between hair loss and regular hair fall. Diagnosing hair related diseases is time-consuming as it requires professional dermatologists to perform visual and medical tests. There are some works reported in literature which perform binary classification of whether the person has alopecia or not. Often, clinicians calculate SALT, Ludwig or Norwood scores to detect the stage of alopecia. This would require a patient's scalp view from top, front, sides and back, which acts as a constraint on some datasets that only have a single view of the patient. Our proposed method of scalp detection, skin and hair region segmentation and alopecia detection can be performed on single view image. As the final deployment device is a memory constrained device, our model uses classical image processing algorithms to segment the hair and skin regions. Our method reported an accuracy of 94% on detecting alopecia on the Dermnet dataset. The lack of segmentation ground truth for hair and skin in Dermnet has motivated us to use the Figaro dataset for evaluating the performance of the proposed segmentation methodClinical relevance- Unlike other reported methods in the literature which just states whether the person has alopecia or not, the proposed method provides a complete insight into the percentage of skin and hair regions on the scalp by performing segmentation of skin and hair regions along with classification. The report generated from the method help the dermatologists to monitor the efficacy of the treatment by comparing varied dated images before and during the treatment cycle. The proposed method can be run on portable devices even with limited memory for monitoring the successfulness of alopecia treatment at home.