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.
