Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Navrati Saxena
Second Advisor
Fabio Di Troia
Third Advisor
Aneesh Verma
Keywords
Chest X-ray, COVID-19, pneumonia, Convolutional neural network, Transfer learning, Feature extraction, Graphical neural network, Graph convolutional network, Graph attention network
Abstract
In clinical practice, it is still difficult to accurately diagnose lung diseases from chest X-ray (CXR) images. In this study, we propose a new hybrid method for identifying several types of lung diseases using CXR images by combining Convolutional Neural Networks (CNNs) with Graph Neural Networks (GNNs). The framework of our proposed methodology takes advantage of CNN’s ability to extract detailed visual features and GNN’s capacity to understand complex relationships between these features, enabling comprehensive analysis in a multi-class classification setting of COVID-19, pneumonia, and normal lung conditions. We used multiple transfer learning models such as DenseNet201, VGG16, VGG19, MobileNetV2, and ResNet50 as backbones for extracting rich feature representations from the CXR images. We used K-Means clustering on the extracted CNN feature vectors to transform them into a graph structure in which clusters were nodes with edges representing similarity based on cluster association for images sharing similar pathological features. Instead of selecting a single CNN model, we experimented with various CNN-GNN hybrid combinations to capture local visual characteristics as well as global relational patterns. As a part of our GNN model, we have developed a Graph Convolutional Network (GCN) hybridized with the Graph Attention Network (GAT). This GCN-GAT hybrid model used the graph structure to uncover complex patterns by analyzing the connections between features to perform lung disease classification. Our best performing hybrid CNN-GNN model delivered impressive performance metrics, with an accuracy of 98.53%, an F1 score of 98.52%, an ROC-AUC of 99.81%, a recall of 98.53%, and a specificity of 99.26% in the multi-class classification task.
Recommended Citation
Khade, Vinay Vilas, "Multiclass lung disease classification in chest X-ray images: A fine-tuned hybrid CNN-GNN approach using transfer learning and feature extraction" (2024). Master's Projects. 1389.
https://scholarworks.sjsu.edu/etd_projects/1389
