The COVID-19 pandemic has created a global health crisis with millions of infections and fatalities. The continuous emergence of SARS-CoV-2 variants has strained healthcare systems worldwide, leading to shortages of testing kits, medical beds, and expert physicians. This study addresses this challenge by exploring the application of computer-aided diagnosis using deep learning to improve early detection and management of COVID-19. Early and accurate diagnosis is crucial for controlling the spread of the virus and improving patient outcomes. The study aims to evaluate the effectiveness of deep learning models in classifying chest X-ray images as normal, pneumonia, or COVID-19, potentially offering a rapid, cost-effective, and readily accessible diagnostic tool, particularly valuable in situations with limited resources or during future pandemic outbreaks.

Several AI-based techniques, primarily machine learning (ML) and deep learning (DL), have been previously applied to COVID-19 diagnosis from CXR images. Studies have used various CNN architectures like VGG-19, ResNet, DenseNet, and InceptionV3, achieving varying degrees of accuracy (e.g., 80-98%). Some studies also incorporated LSTM networks for improved temporal analysis. However, limitations include the inability to differentiate between COVID-19 and other conditions (like pneumonia), the reliance on large datasets, and the need for more robust model validation. This research builds upon existing work by comparing the performance of VGG-16 and LSTM on a publicly available dataset, aiming to provide a more comprehensive evaluation of these models' effectiveness in multi-class classification.

This study employed a publicly available dataset of chest X-ray images (https://github.com/ieee8023/covid-chestxray-dataset) containing images of normal lungs, COVID-19 cases, and pneumonia cases. The dataset was preprocessed using Python with TensorFlow, Keras, Pandas, NumPy, and matplotlib. Two deep learning models, VGG-16 and LSTM, were implemented and trained on 80% of the data, with the remaining 20% used for testing. Each model underwent training for 200 epochs with a batch size of 32 and a learning rate of 0.0001, utilizing the Adam optimizer and a cross-entropy loss function. The performance of each model was evaluated using metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). The VGG-16 model is a convolutional neural network (CNN) known for its strong feature extraction capabilities, while the LSTM model is a recurrent neural network (RNN) suitable for sequential data processing. The choice of these architectures reflects an attempt to evaluate both image-based and potentially temporal aspects of the data.

Both VGG-16 and LSTM models demonstrated high accuracy in classifying CXR images into normal, COVID-19, and pneumonia. The VGG-16 model achieved higher accuracy (100% for COVID-19 and 99% for pneumonia) based on ROC AUC values and outperformed the LSTM model in terms of precision, recall, and F1-score. Specifically, VGG-16's performance metrics were consistently higher across all three classes (normal, COVID-19, and pneumonia). The confusion matrices for both models revealed detailed performance across different classes. Training and validation curves showed that both models exhibited good convergence. VGG-16 achieved 98.94% training accuracy and 100% validation accuracy after 200 epochs. The LSTM model reached 92% validation accuracy and 88% training accuracy after 200 epochs. The comparative analysis showed that the proposed models, particularly VGG-16, outperformed many existing methods in terms of accuracy, precision, sensitivity, and F1-score, although VGG-16 did involve a significantly higher number of parameters.

The results demonstrate the potential of deep learning for rapid and accurate COVID-19 diagnosis using readily available CXR images. The superior performance of VGG-16 compared to LSTM in this context may be attributed to its ability to effectively extract relevant features from the images. The high accuracy achieved across all three categories (normal, COVID-19, pneumonia) suggests that the models are capable of differentiating between subtle visual patterns associated with each condition. The ability to accurately classify COVID-19 cases, especially with high recall, is crucial for reducing false negatives and ensuring timely intervention. This approach could be particularly valuable in resource-limited settings, where access to advanced diagnostic tools might be restricted. Future research could focus on data fusion to potentially further improve diagnostic performance and expand the model's capabilities to analyze CXR images with multiple simultaneous infections.

This study successfully demonstrates the application of VGG-16 and LSTM for COVID-19 prognosis using CXR images. VGG-16 exhibited superior performance compared to LSTM. The high accuracy achieved highlights the potential of deep learning as a valuable tool for assisting clinicians in rapid and accurate COVID-19 diagnosis, especially during outbreaks. Future work could explore integrating the models into a clinical workflow, expanding the dataset to include more diverse populations and disease severities, and investigating data fusion techniques to improve accuracy and handle cases with mixed infections.

The study's reliance on a publicly available dataset might limit the generalizability of findings. The dataset’s size and composition might not fully represent the diversity of COVID-19 presentations and patient populations encountered in real-world settings. Further validation with larger, more diverse datasets is necessary to confirm the robustness of the models' performance. The study focused on CXR images; incorporating other data modalities (e.g., clinical data) might enhance diagnostic accuracy. Additionally, ethical considerations regarding data privacy and model bias should be addressed in future implementations.