Multimodal Imaging and Deep Learning Fusion for Enhanced COVID-19 Lung Infection Detection

Abstract

The World Health Organization (WHO) classified COVID-19 as a global pandemic due to its rapid and widespread transmission, primarily affecting the respiratory system. As a highly infectious virus, COVID-19 spread worldwide without an immediate cure, making detection and diagnosis a significant challenge. Although laboratory tests have been the primary method for identifying COVID-19, they are often prone to inaccuracies and delays, leading researchers to explore alternative techniques. Medical imaging, particularly through Computed Tomography (CT) and radiological scans, has proven to be a valuable diagnostic tool. By analyzing both normal and COVID-19-infected chest CT scans, these images can effectively classify cases, potentially reducing the reliance on large labeled datasets while still ensuring accurate detection and differentiation between COVID-19 and non-COVID-19 cases. COVID-19 is highly contagious and especially dangerous for vulnerable populations, including individuals over 60 or those with pre-existing health conditions. Common symptoms include fever, cough, shortness of breath, and respiratory difficulties. Severe cases can lead to pneumonia, acute respiratory distress syndrome, kidney failure, and even death. Preventive measures such as frequent handwashing, covering the mouth and nose when coughing or sneezing, thoroughly cooking food, and avoiding close contact with individuals exhibiting respiratory symptoms are essential in controlling its spread. Early detection of COVID-19 is crucial for timely treatment and containment of the disease. This study explores the application of machine learning techniques to detect COVID-19 using three common medical imaging modalities: X-ray, ultrasound, and CT scans. A preliminary comparison of widely used Convolutional Neural Network (CNN) models was conducted, identifying VGG19 as a suitable candidate for further optimization. By adapting VGG19 to the distinct characteristics of these imaging modalities, we demonstrate how the model can handle the challenges of scarce datasets in COVID-19 diagnosis. Deep learning, which replicates the human brain’s ability to tackle complex problems through artificial neural networks, plays a central role in this approach. However, deep learning models typically require extensive labeled datasets, which are difficult to acquire in the case of COVID-19. To address this limitation, our study presents a semi-supervised architecture utilizing the Inception v3 model to classify COVID-19 from chest CT scan images. Additionally, Long Short-Term Memory (LSTM) networks are integrated to capture axial dependencies in time-series data. This approach aims to minimize image noise, allowing the deep learning model to concentrate on disease-specific features, ultimately enhancing the accuracy of COVID-19 detection.