BACKGROUND
Alopecia areata is an auto-immune disorder that involves non-scarring hair loss in well-defined patches as well as affecting the entire scalp region and ultimately leads to baldness. The latest worldwide statistics have exhibited that Alopecia areata affects millions of people. Furthermore, the use of conventional methods often leads to poor diagnosis of Alopecia ultimately increasing the medical financial burden on the population. It has been reported that 85% of the individuals suffering from Alopecia areata complain about significant financial burden along with associated costs that are beyond cosmetic concerns. Many individuals adhere to treatment discontinuation owing to enhanced expenses and poor diagnosis.
OBJECTIVE
The objectives of the study comprise of utilizing datasets of healthy hairs and Alopecia areata, extracting color, texture and shape features from the images and applying machine learning algorithms including support vector machine (SVM) and k-nearest neighbor (KNN).
METHODS
Two datasets with images of healthy hairs and Alopecia areata have been utilized. A total of 200 healthy hair images were retrieved from Figaro1k dataset. A total of 68 images of Alopecia areata were retrieved from a dataset known as Dermnet. The images initially go through pre-processing steps including enhancement and segmentation. Following image segmentation, three features of color, texture and shape are extracted. Following feature extraction, machine learning algorithms including support vector machine (SVM) and k-nearest neighbor (KNN) are applied that aid in classifying Alopecia areata and healthy hairs.
RESULTS
A total of 81 images are tested with support vector machine (SVM) and k- nearest neighbor (KNN) yielding an accuracy of 91.4% and 88.9% respectively. The results of the paired sample T-test via SPSS analysis demonstrate a p < 0.001 and exhibits that the accuracies acquired from the two machine learning techniques are significantly different. The accuracies reported will enable a hair expert in recommending a suitable diagnosis and hair treatment regimen to a patient.
CONCLUSIONS
The application of support vector machine (SVM) presented an accuracy of 91.4% and that of k-nearest neighbor (KNN) presented an accuracy of 88.9%. These accuracies exhibit that the proposed classification framework is found to be successful and robust. However, future work with deep learning techniques such as convolutional neural networks (CNN) can be also be carried out and integrated with the existing system.
