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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | IMPLEMENTATION OF A MOBILE SKIN INFECTION DIAGNOSIS SYSTEM USING DEEP-LEARNING |
| Authors Name: | Kinga Mary Temidayo , Olutayo Kehinde Boyinbode , Adetomokun Israel Adebowale |
| Download E-Certificate: | Download |
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IJNRD_216545
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| Published Paper Id: | IJNRD2404722 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Skin Infection is the most common type of infection. It is caused by multiple reasons, some of which includes bacteria, allergies, viruses, etc. The advancement of computer technologies in addition to improvement in the medical field makes it possible to diagnose skin infections quickly and more accurately. Due to increase in the cost of diagnosis in hospitals and the time constraints involved, image classification techniques plays a major role in skin infection diagnosis. The image classification process makes use of feature extraction technique to help in the skin infection diagnosis process. Convolutional Neural Network has played a major role in the feature extraction process. This paper contributes to the research of skin infection detection and the introduction of first-aid steps depending on what kind of infection is present. The image classification process takes the image of the infected area, and then uses image classification to classify the infection based on the infection type. The proposed approach is easy to use and does not require much equipment, other than a mobile device with a working camera and a computer. The approach works by comparing the captured image with the set of pre-trained images using Mobile-net. Finally the results are displayed to the user, including the type of infection, a step by step first aid process and more information on the type of infection. This system successfully detects 3 types of skin infections with 68% accuracy. |
| Keywords: | Skin Infection, Deep-Learning, Convolutional Neural Network, Mobile-Net, Image Classification. |
| Cite Article: | "IMPLEMENTATION OF A MOBILE SKIN INFECTION DIAGNOSIS SYSTEM USING DEEP-LEARNING", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.h171-h179, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404722.pdf |
| Downloads: | 00039 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404722 Registration ID: 216545 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: h171-h179 Country: Akure, Ondo State, Nigeria Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404722 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404722 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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