Methods of Transfer Learning for Multiclass Hair Disease Categorization

In the field of dermatology, skin disorders, particularly hair-related conditions, present a significant challenge. Image-based automated categorization of hair problems has gained a significant research attention due to its potential to assist dermatologists in the process of early diagnosis and treatment planning. Transfer learning, a technique that utilizes pre-trained deep neural networks, has proven to be valuable in various computer vision applications. This study investigates the application of transfer learning for leveraging a multiclass classification of hair disorders by utilizing the three commonly used Convolutional Neural Network (CNN) architectures: AlexNet, VGG16, and ResNet50. This research study begins with the collection of a comprehensive dataset comprising high-resolution images of various hair disorders, including but not limited to alopecia areata, tinea capitis, and androgenetic alopecia. Categorizing the dataset into different groups based on the type and severity of each condition enables a thorough evaluation of the models. Transfer learning approach is employed for fine-tuning these neural network architectures by using the hair disease dataset. A hyperparameter tuning strategy is also adopted to optimize different parameters such as learning rates, batch sizes, and optimization methods to enhance the model performance. The results reveal that all three architectures, including ResNet50, achieve a 99% accuracy rate in classifying multiclass hair diseases. Such technology has the potential to assist dermatologists in their clinical practice by enabling rapid and precise disease detection, thereby improving patient outcomes and healthcare efficiency. Further research could explore the integration of these models into clinical workflows and their application in telemedicine for enabling remote diagnosis and consultation.