Diagnosis And Prognosis Of COVID-19 Using Volumetric Three-Dimensional Computed Tomography Images

Abstract

This research presents two key contributions aimed at improving COVID-19 severity prediction, specifically intubation or death within one month using 3D CT scan data. First, we introduce a novel dataset of 2,000 segmented 3D lung cubes meticulously curated from the STOIC dataset through a robust 10-step preprocessing and segmen tation pipeline. It is evident that 3D CNNs outperform 2D CNNs in this domain, owing to their ability to capture inter-slice information in 3D images, while Vision Transformers excel in texture-based classification tasks. Therefore, as second contri bution we propose two distinct methods for predicting COVID-19 severity, defined as intubation or death within one month. The first method employs a 3D-CNN pre trained on the MosMedData dataset, later fine-tuned on the STOIC dataset with two input layers: one for 3D lung images and another for age and gender metadata. The second method know as 3D-EffiBOT leverages a combination of 3D EfficientNetV2 and iBOT architectures to capture both 3D as well as 2D spatial features from vol umetric CT scans. 3D EfficientNetV2 with weights obtained after inflating 2D Ima geNet weights, was fine-tuned on the STOIC dataset using a dynamic layer unfreez ing strategy, while iBOT was employed to extract 2D slice-level features from axial CT slices. Both models were trained using five augmentation techniques and evalu ated using stratified 5-fold sampling to address class imbalance, achieving mean AUC score of 0.7862 and 0.7414 for 3D-EffiBOT and 3D-CNN respectively. This work demonstrates the effectiveness of hybrid architectures in medical imaging, offering a significant improvement over conventional method. The results suggest that combin ing advanced 3D and 2D feature extractors enhances diagnostic accuracy, providing a valuable tool for predicting severe COVID-19 outcomes. Future research directions include integrating patient pre-COVID medical history, expanding the model’s appli cation to other diseases, and exploring ensemble learning for improved performance across diverse populations.