Hair and scalp disease detection using deep learning
Kavita Sultanpure Bhairavi Shirsath Bhakti Bhande Harshada Sawai Srushti Gawade Suraj Samgir
In recent years, there has been a notable advancement in the integration of
healthcare and technology, particularly evident in the field of medical image
analysis. This paper introduces a pioneering approach in dermatology,
presenting a robust method for the detection of hair and scalp diseases using
state-of-the-art deep learning techniques. Our methodology relies on
Convolutional Neural Networks (CNNs), well-known for their efficacy in image
recognition, to meticulously analyze images for various dermatological
conditions affecting the hair and scalp. Our proposed system represents a
significant advancement in dermatological diagnostics, offering a non-invasive
and highly efficient means of early detection and diagnosis. By leveraging the
capabilities of CNNs, our model holds the potential to revolutionize
dermatology, providing accessible and timely healthcare solutions. Furthermore,
the seamless integration of our trained model into a web-based platform
developed with the Django framework ensures broad accessibility and usability,
democratizing advanced medical diagnostics. The integration of machine learning
algorithms into web applications marks a pivotal moment in healthcare delivery,
promising empowerment for both healthcare providers and patients. Through the
synergy between technology and healthcare, our paper outlines the meticulous
methodology, technical intricacies, and promising future prospects of our
system. With a steadfast commitment to advancing healthcare frontiers, our goal
is to significantly contribute to leveraging technology for improved healthcare
outcomes globally. This endeavor underscores the profound impact of
technological innovation in shaping the future of healthcare delivery and
patient care, highlighting the transformative potential of our approach.