# Convolutional Neural Network with Feature Extraction to Improve the Classification Accuracy of Multi-Class Facial Skin Disorders
> Rismayani

URL kanonis: https://discover.unhas.ac.id/publications/convolutional-neural-network-with-feature-extraction-to-improve-the-classificati
Jurnal / Konferensi: International Journal of Online and Biomedical Engineering
Tahun terbit: 2025
DOI: https://doi.org/10.3991/ijoe.v21i03.52631
ISSN: 26268493
Kuartil SJR: Q2
Citations: 1

## Authors
- Rismayani

## Abstract
This study aims to improve the accuracy of multi-class facial skin disorder classification using a convolutional neural network (CNN) enhanced with feature extraction. The CNN method for classifying multi-class facial skin disorders uses color feature extraction using color moment (CM) and Laplacian of Gaussian (LoG) for direct shape with image data. Multi-class facial skin disorders include oily, hyperpigmentation, acne, redness, blackhead, and normal. A public dataset is used with 7151 images with a balanced number of data classes. Researchers divided the data set into 80% for training and 20% for testing. Experiments are carried out through training and testing with 100 epochs, resulting in an accuracy of 85% for CNN, 66% for the CM-CNN, 80% for LoG-CNN, and 91% for CM-LoG-CNN. The highest classification accuracy is achieved with the CM-LoG-CNN combination.

## Keywords
- Convolutional neural network
- Pattern recognition (psychology)
- Artificial intelligence
- Computer science
- Feature extraction
- Class (philosophy)
- Feature (linguistics)
- Linguistics
- Philosophy

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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