# Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data

arXiv: https://arxiv.org/abs/2412.20345 · HTML: https://mlsanity.com/paper/2412.20345/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training](https://mlsanity.com/paper/2412.04718/) (2024)
- [Breast Cancer Image Classification Method Based on Deep Transfer Learning](https://mlsanity.com/paper/2404.09226/) (2024)
- [Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification](https://mlsanity.com/paper/2411.12151/) (2024)
- [LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning](https://mlsanity.com/paper/2411.09947/) (2024)
- [Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparison](https://mlsanity.com/paper/2412.05536/) (2024)
- [Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs](https://mlsanity.com/paper/2411.15195/) (2024)
- [Optimizing Gesture Recognition for Seamless UI Interaction Using Convolutional Neural Networks](https://mlsanity.com/paper/2411.15598/) (2024)
- [Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data](https://mlsanity.com/paper/2411.18622/) (2024)
- [Accurate Medical Named Entity Recognition Through Specialized NLP Models](https://mlsanity.com/paper/2412.08255/) (2024)
- [Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models](https://mlsanity.com/paper/2412.03606/) (2024)
- [Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models](https://mlsanity.com/paper/2412.06249/) (2024)

## Built on by (most cited first, 3 of 3)

- [Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation](https://mlsanity.com/paper/2502.03813/) (2025)
- [Multi-Scale Transformer Architecture for Accurate Medical Image Classification](https://mlsanity.com/paper/2502.06243/) (2025)
- [A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization](https://mlsanity.com/paper/2503.06888/) (2025)

## Abstract (arXiv)

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.