Res-Dense Net for 3D Covid Chest CT-scan classification
This work addresses the problem of rapid and accurate COVID-19 diagnosis for medical imaging applications, but it appears incremental as it combines existing methods.
The paper tackled COVID-19 diagnosis from 3D CT-scan images by proposing a stacking deep neural network method using DenseNet 121 and ResNet 101 backbones, achieving competitive performance on evaluation metrics.
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