The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2024.0150668
PDF

Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach

Author 1: Nor Surayahani Suriani
Author 2: Syaidatus Syahira Ahmad Tarmizi
Author 3: Mohd Norzali Hj Mohd
Author 4: Shaharil Mohd Shah

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 6, 2024.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

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.

Keywords: Acne detection; severity level; MobileNetV2; convolutional neural network

Nor Surayahani Suriani, Syaidatus Syahira Ahmad Tarmizi, Mohd Norzali Hj Mohd and Shaharil Mohd Shah, “Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 15(6), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150668

@article{Suriani2024,
title = {Acne Severity Classification on Mobile Devices using Lighweight Deep Learning Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150668},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150668},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {Nor Surayahani Suriani and Syaidatus Syahira Ahmad Tarmizi and Mohd Norzali Hj Mohd and Shaharil Mohd Shah}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org