Evaluating VGG16 and Mobilenetv2 for Automatic Recognition of Art Periods Using Transfer Learning (Published)
Artificial intelligence offers new ways to organize and share cultural heritage collections. This paper evaluates its potential for automatically classifying paintings by art period, as a tool for the digitization and accessibility of art-related information. Two convolutional neural network (CNN) models, VGG16 and MobileNetV2, were adapted through transfer learning from ImageNet-pretrained weights and trained on the WikiArt dataset, which contains 81,444 paintings across 27 art periods. Class imbalance was addressed with data augmentation and class weights, and the data were split into stratified training, validation and test sets (70/15/15). VGG16 achieved the best performance, with an AUC of 0.966, an accuracy of 0.763 and an F1-score of 0.701, compared with 0.946, 0.703 and 0.699 for MobileNetV2. MobileNetV2, however, trained faster and used less memory. The results reveal a trade-off between accuracy and efficiency that can guide the choice of model for galleries, museums and educational platforms.
Keywords: Computer Vision, MobileNetV2, VGG16, art period classification, convolutional neural networks, transfer learning
A Comparative Study on the Detection of Pneumonia in Chest X-Ray Images Utilizing Deep Learning Models (Published)
Pneumonia continues to pose a notable public health issue on a global scale, highlighting the crucial need for precise and prompt identification to lessen its effects on patient prognosis. Chest X-ray imaging is a common diagnostic tool for identifying pneumonia due to its non-invasiveness and wide availability. Recently, convolutional neural networks (CNNs), a type of deep learning method, have shown promise in automating the detection of pneumonia from X-ray images. In this paper, we present a comprehensive comparative study of three popular deep learning models—VGG16, Inception V3, and ResNet 50V2—for pneumonia detection in X-ray datasets. The dataset used consists of 5,863 chest X-ray images collected from Kaggle, which are classified into two main categories: pneumonia and normal. The ResNet 50V2 model did very well in the experiments; it was able to correctly identify pneumonia 95.97% of the time, which was better than both VGG16 (93.58%) and Inception V3 (93.38%). We also conduct an analysis of performance metrics such as validation loss, validation accuracy, recall, and precision, and calculate the area under the receiver operating characteristic (ROC) curve (AUC) for each model. We also talk about how to compare ROC curves and precision-recall curves, which lets you see how well the models do at telling the difference between things and how well they do across a number of evaluation metrics. Our study contributes to the development of efficient and high-performance deep learning models for improving the diagnosis and treatment processes for pneumonia patients in the medical field.
Keywords: Chest X-ray, Comparative Analysis, Inception V3, Pneumonia, ResNet 50V2, VGG16, convolutional neural networks, deep learning