• Corpus ID: 236950728

A Data Augmented Approach to Transfer Learning for Covid-19 Detection

  title={A Data Augmented Approach to Transfer Learning for Covid-19 Detection},
  author={Shagufta Henna and A. P. Reji},
Covid-19 detection at an early stage can aid in an effective treatment and isolation plan to prevent its spread. Recently, transfer learning has been used for Covid-19 detection using X-ray, ultrasound, and CT scans. One of the major limitations inherent to these proposed methods is limited labeled dataset size that affects the reliability of Covid-19 diagnosis and disease progression. In this work, we demonstrate that how we can augment limited X-ray images data by using Contrast limited… 

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.



COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs

This work presented a computationally efficient and highly accurate model for multi-class classification of three different infection types from along with Normal individuals that can help in the early screening of COVID19 cases and help reduce the burden on healthcare systems.

A deep convolutional neural network for COVID-19 detection using chest X-rays

The state-of-the-art performances achieved show that chest X-rays can become a cheap and accurate auxiliary method for COVID-19 diagnosis and improve the interpretability of the deep neural networks and indicate an analytical path for future research on diagnosis.

Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays

Use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestations of COVID-19 with chest X-rays and the combined use of modality-specific knowledge transfer, iterative model pruning, and ensemble learning resulted in improved predictions.

COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images

COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest radiography images that is open source and available to the general public and investigated how it makes predictions using an explainability method.

Deep Learning for Screening COVID-19 using Chest X-Ray Images

A new concept called domain extension transfer learning (DETL) is proposed, with pre-trained deep convolutional neural network, on a related large chest X-Ray dataset that is tuned for classifying between four classes viz. normal, pneumonia, other_disease, and Covid – 19.

COVID-19 Detection from Chest X-ray Images using CNNs Models: Further Evidence from Deep Transfer Learning

This study revisits the identification of COVID-19 from chest x-ray images using Deep Learning and fine-tune three pre-trained deep convolutional neural networks (CNNs) models on a training dataset; DenseNet 121, NASNetLarge and NASNetMobile.

Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest X-Ray Images

An open-source dataset of COVID-19 CXR-Dataset is presented, and a deep convolutional neural network model is introduced, which validates on 740 test images and achieves 87.3% accuracy, 89.67 % precision, and 84.46% recall.

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has

Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset

This preliminary study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size will enable a better answer to the question of how useful chest X-ray images can be for diagnosing CO VID-19.