Res-Dense Net for 3D Covid Chest CT-scan classification

TL;DR

This paper introduces a stacking deep neural network approach utilizing DenseNet 121 and ResNet 101 backbones for classifying 3D COVID-19 chest CT scans, aiming to improve rapid diagnosis accuracy.

Contribution

It proposes a novel combination of DenseNet and ResNet architectures in a stacking framework for COVID-19 detection from 3D CT scans.

Findings

Achieved competitive performance on evaluation metrics

Demonstrated effectiveness of combining DenseNet and ResNet backbones

Validated approach for rapid COVID-19 diagnosis using 3D CT images

Abstract

One of the most contentious areas of research in Medical Image Preprocessing is 3D CT-scan. With the rapid spread of COVID-19, the function of CT-scan in properly and swiftly diagnosing the disease has become critical. It has a positive impact on infection prevention. There are many tasks to diagnose the illness through CT-scan images, include COVID-19. In this paper, we propose a method that using a Stacking Deep Neural Network to detect the Covid 19 through the series of 3D CT-scans images . In our method, we experiment with two backbones are DenseNet 121 and ResNet 101. This method achieves a competitive performance on some evaluation metrics