AN EXPLORATION OF 2D AND 3D CT SCAN IMAGES FOR DETECTING COVID-19 USING SPATIAL CONVOLUTIONAL NEURAL NETWORK

COVID-19 has been the most impactful pandemic in modern history, affecting people all across the world. The identification of COVID-19 relies mostly on lung Computer Tomography (CT) images. A computer-aided diagnosis (CAD) method for classifying COVID-19 CT images is presented in this study. In this study, assess the accuracy of 2D and 3D SCNN models that share similar architectural details. The dataset is sourced from the 2D and 3D CT scan images Dataset, Chest CT scans with COVID-19 related findings database. Threshold segmentation is the best method for separating the chest from the rest of the CT scan. An advanced collection of deep learning models, SCNN combines the best 2D and 3D systems. It combines slice-level assessments, a CNN model, and unique preprocessing and attention components. In light of this, the suggested study introduces SCNN, an image-processing-based COVID-19 detection model. The COVID-19 CT images used to train this model were split into three categories: COVID-19, pneumonia, and healthy subjects. If the input image does not include the required attributes, an image preprocessing pipeline may be used to extract the ROI. As part of the proposal, combine the predictions made by 2D and 3D SCNN models. Using contrastive learning and an attention mechanism, this work presents a classification method. By reducing the distance between images in the same category, contrastive learning may increase the feature space used for classification. To aid with classification, an attention mechanism may draw focus on a key area of the image while offering a visual representation of that area. We showed a significant increase in classification accuracy by studying SCNN classification. In addition, we have achieved a comprehensive visual representation as compared to conventional methods.