Hair Disease Classification Using Convolutional Neural Network Algorithm with VGG-16 Architecture

October 2023 in “ Sinkron ”

Ichwanul Muslim Karo Karo, Dedi Kiswanto, Suvriadi Panggabean, Adidtya Perdana

hair disease Convolutional Neural Network CNN VGG-16 image preprocessing deep learning healthcare

TLDR The system can accurately classify hair diseases with 94.5% accuracy using a CNN.

This research developed a hair disease classification system using a Convolutional Neural Network (CNN) with VGG-16 architecture, achieving an accuracy of 94.5% and a loss rate of 18.47% over 15 epochs. The study utilized a dataset of annotated hair disease images from Kaggle, processed through image preprocessing stages. The CNN-based approach aims to assist health professionals in accurately diagnosing and treating hair diseases, demonstrating the potential of deep learning in healthcare.

View this study on jurnal.polgan.ac.id →