Towards detection of alopecia diseases through deep learning

Abstract

The frequency of alopecia, a group of illnesses characterized by hair loss, makes diagnosis and treatment extremely difficult. Conventional diagnosis techniques produce outcomes that are subjective and inconsistent since they mainly rely on eye inspection and manual assessment. In this thesis, we provide a unique method for alopecia identification that makes use of deep learning methods. Through the application of CNN algorithm, our vision is to make an automated system which can recognize different types of alopecia from digital photographs of the scalp with high accuracy. We applied KNN and SVM too and found limitation of encoding. Our approach begins with gathering a large dataset of various alopecia cases, then preprocessing and augmenting it to improve model generalization. Subsequently, we design and train CNN architectures optimized for feature extraction and classification of Alopecia patterns. Through extensive experimentation and evaluation on both synthetic and real-world datasets, we demonstrate the effectiveness and robustness of our proposed framework in discriminating between different types and stages of alopecia with high accuracy and reliability. Our findings suggest promising implications for the integration of deep learning technologies in clinical settings to facilitate early diagnosis, personalized treatment planning, and monitoring of alopecia-related conditions, thereby improving patient outcomes and quality of care.