Integrating Deep Learning with Dermoscopy for Enhanced Detection of Scalp and Hair Follicle Anomalies

Hair fall is the common issue for many people worldwide. Almost 50% of Indian men and 20-30% ofIndian women experience hair loss in any form in their lifetime. Hair loss occurs due to many factors such as aging,stress, medication, etc. Hair fall and related diseases often go unnoticed in the beginning and patients find itdifficult to differentiate between hair loss and a regular hair fall. When the situation gets worsened, then they getaware of the illness. When they consult a dermatologist, then the diagnosis gets delayed. Due to the latest DeepLearning (DL) technologies and its applications, it is easy to assist Dermatologists with faster disease detectionand diagnosis. In this research, 10 diseases are used for detection namely Alopecia Areata, Contact Dermatitis,Folliculitis, Head Lice, Lichen Planus, Male Pattern Baldness, Psoriasis, Seborrheic Dermatitis, Telogen Effluviumand Tinea Capitis. 12000 images are divided into 10 classes containing 1200 images in each class. Images werepreprocessed by denoising, enhancement and data augmentation. Hyperparameter tuning, fine tuning andregularization was also done to make the model more precise with learning rate of 0.0001, pretrained VGG16model and Dropout probability of 0.5. Overall training accuracy of 99% with a validation accuracy of 93% isobtained.