Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data
Journal:
arXiv
Published Date:
Dec 29, 2024
Abstract
This study aims to explore the automatic classification method of pneumonia
X-ray images based on VGG19 deep convolutional neural network, and evaluate its
application effect in pneumonia diagnosis by comparing with classic models such
as SVM, XGBoost, MLP, and ResNet50. The experimental results show that VGG19
performs well in multiple indicators such as accuracy (92%), AUC (0.95), F1
score (0.90) and recall rate (0.87), which is better than other comparison
models, especially in image feature extraction and classification accuracy.
Although ResNet50 performs well in some indicators, it is slightly inferior to
VGG19 in recall rate and F1 score. Traditional machine learning models SVM and
XGBoost are obviously limited in image classification tasks, especially in
complex medical image analysis tasks, and their performance is relatively
mediocre. The research results show that deep learning, especially
convolutional neural networks, have significant advantages in medical image
classification tasks, especially in pneumonia X-ray image analysis, and can
provide efficient and accurate automatic diagnosis support. This research
provides strong technical support for the early detection of pneumonia and the
development of automated diagnosis systems and also lays the foundation for
further promoting the application and development of automated medical image
processing technology.
