Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach
Acne is a prevalent skin condition affecting millions of people globally, impacting not just physical health but also mental well-being. Early detection of skin diseases such as acne is important for making treatment decisions to prevent the spread of the disease. The main goal of this project is to...
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| Language: | en |
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ijacsa
2024
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| Online Access: | http://eprints.uthm.edu.my/12465/1/J17954_25233101fb10841f197b4bdf2f3781ce.pdf http://eprints.uthm.edu.my/12465/ |
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| author | Nor Surayahani Suriani, Nor Surayahani Suriani Ahmad Tarmizi, Syaidatus Syahira Hj Mohd, Mohd Norzali Mohd Shah, Shaharil |
| author_facet | Nor Surayahani Suriani, Nor Surayahani Suriani Ahmad Tarmizi, Syaidatus Syahira Hj Mohd, Mohd Norzali Mohd Shah, Shaharil |
| author_sort | Nor Surayahani Suriani, Nor Surayahani Suriani |
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| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Acne is a prevalent skin condition affecting millions of people globally, impacting not just physical health but also mental well-being. Early detection of skin diseases such as acne is important for making treatment decisions to prevent the spread of the disease. The main goal of this project is to develop an Android mobile application with deep learning that allows users to diagnose skin diseases and also detect the severity level of skin diseases in three levels: mild, moderate, and severe. Most of the deep learning methods require devices with high computational resources which hardly implemented in mobile applications. To overcome this problem, this research will focus on lightweight Convolutional Neural Networks (CNN). This study focuses on the efficiency of MobileNetV2 and Android applications that are used
in this project to detect skin diseases and severity levels. Android Studio is used to create a GUI interface, and the model works perfectly and successfully by using TensorFlow Lite. The skin disease images of acne with severity levels (mild, moderate, and severe) achieve 92% accuracy. This study also demonstrated good results when it was implemented on an Android application through live camera input. |
| format | Article |
| id | my.uthm.eprints-12465 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2024 |
| publisher | ijacsa |
| record_format | eprints |
| spelling | my.uthm.eprints-124652025-02-13T02:48:15Z http://eprints.uthm.edu.my/12465/ Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach Nor Surayahani Suriani, Nor Surayahani Suriani Ahmad Tarmizi, Syaidatus Syahira Hj Mohd, Mohd Norzali Mohd Shah, Shaharil RL Dermatology Acne is a prevalent skin condition affecting millions of people globally, impacting not just physical health but also mental well-being. Early detection of skin diseases such as acne is important for making treatment decisions to prevent the spread of the disease. The main goal of this project is to develop an Android mobile application with deep learning that allows users to diagnose skin diseases and also detect the severity level of skin diseases in three levels: mild, moderate, and severe. Most of the deep learning methods require devices with high computational resources which hardly implemented in mobile applications. To overcome this problem, this research will focus on lightweight Convolutional Neural Networks (CNN). This study focuses on the efficiency of MobileNetV2 and Android applications that are used in this project to detect skin diseases and severity levels. Android Studio is used to create a GUI interface, and the model works perfectly and successfully by using TensorFlow Lite. The skin disease images of acne with severity levels (mild, moderate, and severe) achieve 92% accuracy. This study also demonstrated good results when it was implemented on an Android application through live camera input. ijacsa 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/12465/1/J17954_25233101fb10841f197b4bdf2f3781ce.pdf Nor Surayahani Suriani, Nor Surayahani Suriani and Ahmad Tarmizi, Syaidatus Syahira and Hj Mohd, Mohd Norzali and Mohd Shah, Shaharil (2024) Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach. International Journal of Advanced Computer Science and Applications,, 15 (6). pp. 680-687. |
| spellingShingle | RL Dermatology Nor Surayahani Suriani, Nor Surayahani Suriani Ahmad Tarmizi, Syaidatus Syahira Hj Mohd, Mohd Norzali Mohd Shah, Shaharil Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach |
| title | Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach |
| title_full | Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach |
| title_fullStr | Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach |
| title_full_unstemmed | Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach |
| title_short | Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach |
| title_sort | acne severity classification on mobile devices using lighweight deep learning approach |
| topic | RL Dermatology |
| url | http://eprints.uthm.edu.my/12465/1/J17954_25233101fb10841f197b4bdf2f3781ce.pdf http://eprints.uthm.edu.my/12465/ |
| url_provider | http://eprints.uthm.edu.my/ |