@inproceedings{41aa6c80549a41f1afdf80d27ccc7745,
title = "Teledermatologist: Automated Diagnosis of Skin Diseases with Image Recognition",
abstract = "Early diagnosis of skin diseases is crucial for effective treatment, preventing spread, minimizing long-Term damage, managing chronic conditions, detecting underlying health issues, and promoting psychological well-being. It is often necessary to do difficult tests in order to obtain a diagnosis, which demands a lot of resources from patients as well as medical personnel. Motivated by this shortage, we introduce a user-friendly and private system for the automatic detection of skin disease from a picture. Our system employs a deep neural model for the accurate classification of a given picture to one of the predefined classes of diseases. We created a large dataset of six skin lesion types and tested different neural models. The best-performing model, which achieved 92% accuracy, was integrated into our system. The application and its web demo can be accessed at https://teledermatologis-Ai.streamlit.app/.",
keywords = "Dataset, Facial Skin Disease, Image Recognition",
author = "Nadav Ishai and Dolev Peretz and Natalia Vanetik and Marina Litvak",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023 ; Conference date: 04-12-2023 Through 07-12-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1109/VCIP59821.2023.10402645",
language = "English",
series = "2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023",
address = "United States",
}