International Journal on Science and Technology

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Skin Condition Using Streamlit

Author(s) Y. Poojitha, G. Suma Latha, P. Surekha, K. Ruchitha, Dr. R. Ashok Kumar Reddy
Country India
Abstract Intelligent imaging-based medical classification systems are beneficial to humans. better medical care choices and infection conclusion for patient’s Computer- aided skin condition classification has recently gained popularity as a result of its importance for the early detection of skin conditions. the research area focal thought of this paper is a framework that utilizes convolutional brain networks for ordering skin sores in variety pictures. It is founded on an existing six skin conditions can be trained using a deep convolutional neural network. classified as vitiligo, athlete's foot, chickenpox, eczema, skin cancer, and eczema. Additionally, we developed a dataset with 3000 coloured images. from a variety of online datasets and the Internet. The discoveries of the proposed model were more accurate than, which is encouraging. the most recent research in this field, with an accuracy of 81.75 %. This Using the holdout method, precision was determined, with ninety Out-of-sample accuracy, percent of the images were used for training Ten percent of the images were tested.
Field Engineering
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-16
Cite This Skin Condition Using Streamlit - Y. Poojitha, G. Suma Latha, P. Surekha, K. Ruchitha, Dr. R. Ashok Kumar Reddy - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3824
DOI https://doi.org/10.71097/IJSAT.v16.i2.3824
Short DOI https://doi.org/g9f2gp
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