Hair Disease Classification Using Convolutional Neural Network Algorithm with VGG-16 Architecture
October 2023
in “
Sinkron
”
Studysummary This study demonstrated that a CNN-based model using VGG-16 architecture achieved a 94.5% accuracy in classifying ten types of hair diseases, implying a promising tool for aiding health professionals in diagnosing hair conditions accurately. Our plain-language summary of this paper — not a Tressless recommendation.
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.
