• Corpus ID: 244478617

Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images

  title={Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images},
  author={Kenan Morani and Devrim {\"U}nay},
The paper presents a Convolutional Neural Networks (CNN) model for image classification, aiming at increasing predictive performance for COVID-19 diagnosis while avoiding deeper and thus more complex alternatives. The proposed model includes four similar convolutional layers followed by a flattening and two dense layers. This work proposes a less complex solution based on simply classifying 2D CT-Scan slices of images using their pixels via a 2D CNN model. Despite the simplicity in architecture… 

COVID-19 Detection Using Segmentation, Region Extraction and Classification Pipeline

The improved work in this paper proposes efficient pipeline with a potential of having clinical usage for COVID-19 detection and diagnosis via CT images, which includes a segmentation part, a region of interest extraction part, and a classifier part.

Let AI Perform Better Next Time—A Systematic Review of Medical Imaging-Based Automated Diagnosis of COVID-19: 2020–2022

This paper presents an in-depth discussion of the existing automated diagnosis models and notes a total of three significant problems: biased model performance evaluation; inappropriate implementation details; and a low reproducibility, reliability and explainability.



MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

A deep learning approach, based on a CNN-RNN network is presented and its performance on the COVID19-CT-DB database is reported and the results of all main techniques that were developed and used in the ICCV COV19D Competition are presented.

COVID19 Diagnosis using AutoML from 3D CT scans

  • T. Anwar
  • Medicine
    2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
  • 2021
An Automated machine learning (AutoML) technique that requires fewer resources (optimal architecture trials) and time to develop, resulting in the best diagnosis of coronavirus.

Inferring the ecological niche of bat viruses closely related to SARS-CoV-2 using phylogeographic analyses of Rhinolophus species

The results show that the ecological niche of bat viruses related to SARS-CoV2 includes several regions of mainland Southeast Asia whereas the ecological sector is mainly restricted to China, and human populations in Laos, Vietnam, Cambodia, and Thailand appear to be much less affected by the COVID-19 pandemic than other countries of Southeast Asia.

Visual Transformer with Statistical Test for COVID-19 Classification

2-D and 3-D models to predict the COVID-19 of CT scan significantly outperform the state-of-the-art methods and a Convolutional CT scan-Aware Transformer is proposed to discover the context of the slices fully.

A hybrid deep learning framework for Covid-19 detection via 3D Chest CT Images

In this paper, we present a hybrid deep learning framework named CTNet which combines convolutional neural network and transformer together for the detection of COVID-19 via 3D chest CT images. It

Custom Deep Neural Network for 3D Covid Chest CT-scan Classification

A method that custom and combine Deep Neural Network to classify the series of 3D CT-scans chest images and experiment with 2 backbones is DenseNet 121 and ResNet 101 is proposed.

A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images

  • Weijun TanJingfeng Liu
  • Computer Science
    2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
  • 2021
An automatic COVID1-19 diagnosis framework from lung CT-scan slice images that is first preprocessed using segmentation techniques to filter out images of closed lung, and to remove the useless background.

Transparent Adaptation in Deep Medical Image Diagnosis

A novel methodology is presented, in which deep neural architectures that have been trained to provide highly accurate predictions over existing datasets are adapted, in a consistent way, to make predictions over different contexts and datasets.

Longitudinal symptom dynamics of COVID-19 infection

Data was extracted from primary-care electronic health records and nationwide distributed surveys to assess the longitudinal dynamics of symptoms prior to and throughout SARS-CoV-2 infection, with fever, cough and fatigue the most prevalent symptoms.