An Approach to Detect Alopecia Areata Hair Disease Using Deep Learning
January 2021
in “
Lecture notes in networks and systems
”
TLDR Deep learning can accurately detect Alopecia Areata with up to 98.3% accuracy.
Three years ago, a study was conducted to detect Alopecia Areata, a common hair disease, using deep learning. The dataset used in the study consisted of 609 images, with 310 images of normal hair and 299 images of hair infected with Alopecia Areata. The dataset was split into a 7:3 ratio for training and testing data, respectively. Pre-trained models (VGG-16, VGG-19, SqueezeNet, and Inception-V3) were used for feature extraction. Algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), logistic regression, and Naive Bayes were applied, achieving a maximum accuracy of 98.3%. The study highlighted the potential of deep learning in early identification of Alopecia Areata.
