Hair and scalp disease detection using deep learning
<|reference_start|>Hair and scalp disease detection using deep learning: 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.<|reference_end|>
@article{sultanpure2024hair,
    title={Hair and scalp disease detection using deep learning},
    author={Kavita Sultanpure, Bhairavi Shirsath, Bhakti Bhande, Harshada Sawai,
  Srushti Gawade, Suraj Samgir},
    journal={arXiv preprint arXiv:2403.07940},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07940},
    primaryClass={eess.IV cs.CV cs.LG}
}