An IoT-Driven Real-Time Skin Disease Detection System using Machine Learning
Approximately 900 million individuals globally are said to have diseases associated with the skin, thus becoming one of the prevalent diseases in the world. Common conditions in southern Punjab are acne, psoriasis, and fungal infections. Eczema has associated red tones and is usually marked by dryn...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sukkur IBA University
2025-02-01
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| Series: | Sukkur IBA Journal of Computing and Mathematical Sciences |
| Online Access: | https://journal.iba-suk.edu.pk:8089/SIBAJournals/index.php/sjcms/article/view/1541 |
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| Summary: | Approximately 900 million individuals globally are said to have diseases associated with the skin, thus becoming one of the prevalent diseases in the world. Common conditions in southern Punjab are acne, psoriasis, and fungal infections. Eczema has associated red tones and is usually marked by dryness and itchiness. Psoriasis creates reddish patches due to the scale formation and the occurrence of thick patches. There are common forms of fungi infections known as ringworm and jock itch and mostly fungi are favored when in a warm and damp environment. Only through quick detection and treatment can one prevent critical cases of skin problems. Skin diseases prove difficult to diagnose because every skin is different in either the types or textures they hold. Researchers have recommended various early detection methods for them. Their solutions through a range of machine learning algorithms like random forest, naive Bayes, logistic regression, kernel SVM, KNN, and CNN based on which they used detection methods for various skin diseases. Based on the detailed research, the real-time symptoms from an IoT and machine learning model can diagnose the skin diseases. Cameras will take pictures of the skin images. IoT will measure body temperature, and a deep learning algorithm and CNN algorithm predict the diseases. The output is provided on the GUI as an Android application, in order to help them understand whether they have skin diseases and allergies early.
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| ISSN: | 2520-0755 2522-3003 |