COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning

2020-06-16 · Arman Haghanifar, Mahdiyar Molahasani Majdabadi, Younhee Choi, S. Deivalakshmi, Seokbum Ko

One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image. In most of the patients, a chest x-ray contains abnormalities, such as consolidation, which are the results of COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from various sources are collected, and the largest publicly accessible dataset is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized for developing COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.

📄 PDF Abstract BibTeX arXiv:2006.13807

Code (1)

armiro/COVID-CXNet 공식 구현 tf

Tasks

Decision Making Image Classification Multi-class Classification Pneumonia Detection Transfer Learning

Methods 이 논문이 사용한 방법론

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Average Pooling 설명 없음

Batch Normalization 설명 없음

Kaiming Initialization 설명 없음

1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…

Dense Block A Dense Block is a module used in convolutional neural networks that connects *all layers* (with matching feature-map sizes) directly with each other. It was originally…

Global Average Pooling Global Average Pooling is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding…

Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

UMLS-ChestNet: A deep convolutional neural network for radiological findings, differential diagnoses and localizations of COVID-19 in chest x-rays

2020-06-06 · Germán González, Aurelia Bustos, José María Salinas, María de la Iglesia-Vaya 외

In this work we present a method for the detection of radiological findings, their location and differential diagnoses from chest x-rays. Unlike prior works that focus on the detection of few pathologies, we use a hierar…

An efficient method of detection of COVID-19 using Mask R-CNN on chest X-Ray images

2021-07-12 · journal 2021 7 · Soumyajit Podder, Somnath Bhattacharjee, Arijit Roy

Artificial intelligence techniques are used on chest X-ray images for accurate detection of diseases and this paper aims to develop a process which is capable of diagnosing COVID-19 using deep learning methods on X-ray i…

Specificity

HOG + CNN Net: Diagnosing COVID-19 and Pneumonia by Deep Neural Network from Chest X-Ray Images.

2021-07-08 · journal 2021 7 · Mohammad Marufur Rahman, Sheikh Nooruddin, K. M. Azharul Hasan, Nahin Kumar Dey

Coronavirus disease 2019 in short COVID-19 is a contagious disease caused by coronavirus SARS-CoV-2, which has caused a global pandemic and still infecting millions around the globe. COVID-19 has made an enormous impact…

Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images

2020-08-03 · Aakarsh Malhotra, Surbhi Mittal, Puspita Majumdar, Saheb Chhabra 외

With increasing number of COVID-19 cases globally, all the countries are ramping up the testing numbers. While the RT-PCR kits are available in sufficient quantity in several countries, others are facing challenges with …

Semantic Segmentation

COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTs

2021-05-17 · Vignav Ramesh, Blaine Rister, Daniel L. Rubin

Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artificial intelligence models. Most work to dat…

Computed Tomography (CT)Lesion Segmentation