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

• URL: http://arxiv.org/abs/2412.20345v1
• Date: Sun, 29 Dec 2024 04:07:58 GMT
• Title: Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data
• Authors: Weijie He, Tong Zhou, Yanlin Xiang, Yang Lin, Jiacheng Hu, Runyuan Bao,
• Abstract summary: VGG19 deep convolutional neural network is compared with classic models such as SVM, XGBoost, and ResNet50. 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.
• Score: 11.570070267134362
• License:
• 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.

Related papers

• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• COVID-19 Severity Classification on Chest X-ray Images [0.0]
  In this work, we classify covid images based on the severity of the infection. The ResNet-50 model produced remarkable classification results in terms of accuracy 95%, recall (0.94), and F1-Score (0.92), and precision (0.91)
  arXiv  Detail & Related papers  (2022-05-25T12:01:03Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks [0.0]
  We developed a computer solution to support vision in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. In its classification performance, the best performing model demonstrated a validation accuracy of 93% and an F1 score of 0.95.
  arXiv  Detail & Related papers  (2021-12-14T01:59:25Z)
• Deep learning for COVID-19 diagnosis based feature selection using binary differential evolution algorithm [1.332091725929965]
  The new Coronavirus is spreading rapidly and has taken the lives of many people so far. Deep Convolution neural networks are a powerful tool in classifying images. Our results demonstrate the suggested approach is better than recent studies on COVID-19 detection with X-ray images.
  arXiv  Detail & Related papers  (2021-04-15T07:12:58Z)
• Classification of COVID-19 X-ray Images Using a Combination of Deep and Handcrafted Features [0.0]
  We use a combination of deep convolutional and handcrafted features extracted from X-ray chest scans to discriminate between healthy, common pneumonia, and COVID-19 patients. We achieve an accuracy of 0.988 in the classification task with our combined approach compared to 0.963 and 0.983 accuracy for the handcrafted features with SVM and CNN respectively.
  arXiv  Detail & Related papers  (2021-01-19T21:09:46Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations [70.0118756144807]
  This work introduces a general pre-processing step for chest x-ray input into machine learning algorithms. A modified Y-Net architecture based on the VGG11 encoder is used to simultaneously learn geometric orientation and segmentation of radiographs. Results were evaluated by expert clinicians, with acceptable geometry in 95.8% and annotation mask in 96.2%, compared to 27.0% and 34.9% respectively in control images.
  arXiv  Detail & Related papers  (2020-05-08T02:16:17Z)
• Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT [49.09507792800059]
  We propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. We evaluate our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP)
  arXiv  Detail & Related papers  (2020-05-07T06:00:02Z)
• Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning [0.0]
  We show that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy) Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy)
  arXiv  Detail & Related papers  (2020-03-31T16:48:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.