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.
![[img]](http://eprints.uthm.edu.my/style/images/fileicons/text.png) |
Text
J17954_25233101fb10841f197b4bdf2f3781ce.pdf Restricted to Registered users only Download (1MB) | Request a copy |
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Acne detection; severity level; MobileNetV2; convolutional neural network |
| Subjects: | R Medicine > RL Dermatology |
| Divisions: | Faculty of Electrical and Electronic Engineering > FKEE |
| Depositing User: | Mr. Mohamad Zulkhibri Rahmad |
| Date Deposited: | 13 Feb 2025 02:47 |
| Last Modified: | 13 Feb 2025 02:48 |
| URI: | http://eprints.uthm.edu.my/id/eprint/12465 |
Actions (login required)
 |
View Item |