An Improved VGG16 Model for Pneumonia Image Classification

Abstract

Image recognition has been applied to many fields, but it is relatively rarely applied to medical images. Recent significant deep learning progress for image recognition has raised strong research interest in medical image recognition. First of all, we found the prediction result using the VGG16 model on failed pneumonia X-ray images. Thus, this paper proposes IVGG13 (Improved Visual Geometry Group-13), a modified VGG16 model for classification pneumonia X-rays images. Open-source thoracic X-ray images acquired from the Kaggle platform were employed for pneumonia recognition, but only a few data were obtained, and datasets were unbalanced after classification, either of which can result in extremely poor recognition from trained neural network models. Therefore, we applied augmentation pre-processing to compensate for low data volume and poorly balanced datasets. The original datasets without data augmentation were trained using the proposed and some well-known convolutional neural networks, such as LeNet AlexNet, GoogLeNet and VGG16. In the experimental results, the recognition rates and other evaluation criteria, such as precision, recall and f-measure, were evaluated for each model. This process was repeated for augmented and balanced datasets, with greatly improved metrics such as precision, recall and F1-measure. The proposed IVGG13 model produced superior outcomes with the F1-measure compared with the current best practice convolutional neural networks for medical image recognition, confirming data augmentation effectively improved model accuracy.

A novel coarse‐to‐fine computational method for three‐dimensional landmark detection to perform hard‐tissue cephalometric analysis

“…This map was created based on the average positional measurements of the landmarks. Before creating the landmark map, standard DL model VGG16 (Jiang et al, 2021; Tarhini et al, 2020) was used to automatic segment the volume of interest. This model was trained on 30 images only to determine that area of the volume is belonging to the skull or not.…”

“…This map was created based on the average positional measurements of the landmarks. Before creating the landmark map, standard DL model VGG16 (Jiang et al, 2021; T A B L E 1 20 cephalometric landmarks along with their definitions used in the study (Lindner et al, 2016). et al, 2020) was used to automatic segment the volume of interest.…”

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

“…Small convolutional filters are used to build the VGG network. VGG-16 [ 71 ] and VGG-19 [ 72 ] are two different versions of VGG that we used to test our proposed system.…”

Enhancing Computer Image Recognition with Improved Image Algorithms

“…Preprocessing was set up for the image datasets to enhance the preprocessed images. Additionally, an improved version of the VGG16 model (which was originally used in the 2014 ILSVRC challenge) was trained [11]. The introduction of K-Means++ algorithm for data preprocessing, the use of improved bidirectional feature pyramid network structure feature fusion, the use of EIoU loss function to optimize boundary box regression, and the introduction of channel attention mechanism in the convolution unit effectively improve the accuracy of ship detection, and has good robustness and generalization ability.…”