CORONA-Net: Diagnosing COVID-19 from X-ray Images Using Re-Initialization and Classification Networks

The COVID-19 pandemic has been deemed a global health pandemic. The early detection of COVID-19 is key to combating its outbreak and could help bring this pandemic to an end. One of the biggest challenges in combating COVID-19 is accurate testing for the disease. Utilizing the power of Convolutional Neural Networks (CNNs) to detect COVID-19 from chest X-ray images can help radiologists compare and validate their results with an automated system. In this paper, we propose a carefully designed network, dubbed CORONA-Net, that can accurately detect COVID-19 from chest X-ray images. CORONA-Net is divided into two phases: (1) The reinitialization phase and (2) the classification phase. In the reinitialization phase, the network consists of encoder and decoder networks. The objective of this phase is to train and initialize the encoder and decoder networks by a distribution that comes out of medical images. In the classification phase, the decoder network is removed from CORONA-Net, and the encoder network acts as a backbone network to fine-tune the classification phase based on the learned weights from the reinitialization phase. Extensive experiments were performed on a publicly available dataset, COVIDx, and the results show that CORONA-Net significantly outperforms the current state-of-the-art networks with an overall accuracy of 95.84%.

Download Full-text

Comparison of Classification Models Based on Deep learning on COVID-19 Chest X-Rays

Journal of Physics Conference Series ◽

10.1088/1742-6596/2161/1/012078 ◽

2022 ◽

Vol 2161 (1) ◽

pp. 012078

Author(s):

Pallavi R Mane ◽

Rajat Shenoy ◽

Ghanashyama Prabhu

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

State Of The Art ◽

Cost Effective ◽

X Rays ◽

The Novel ◽

Learning Models ◽

X Ray ◽

Chest X Ray ◽

Fine Tune

Abstract COVID -19, is a deadly, dangerous and contagious disease caused by the novel corona virus. It is very important to detect COVID-19 infection accurately as quickly as possible to avoid the spreading. Deep learning methods can significantly improve the efficiency and accuracy of reading Chest X-Rays (CXRs). The existing Deep learning models with further fine tune provide cost effective, rapid, and better classification results. This paper tries to deploy well studied AI tools with modification on X-ray images to classify COVID 19. This research performs five experiments to classify COVID-19 CXRs from Normal and Viral Pneumonia CXRs using Convolutional Neural Networks (CNN). Four experiments were performed on state-of-the-art pre-trained models using transfer learning and one experiment was performed using a CNN designed from scratch. Dataset used for the experiments consists of chest X-Ray images from the Kaggle dataset and other publicly accessible sources. The data was split into three parts while 90% retained for training the models, 5% each was used in validation and testing of the constructed models. The four transfer learning models used were Inception, Xception, ResNet, and VGG19, that resulted in the test accuracies of 93.07%, 94.8%, 67.5%, and 91.1% respectively and our CNN model resulted in 94.6%.

Download Full-text

COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks

10.1101/2020.05.22.20110817 ◽

2020 ◽

Author(s):

Antonios Makris ◽

Ioannis Kontopoulos ◽

Konstantinos Tserpes

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Bacterial Pneumonia ◽

State Of The Art ◽

Classification Performance ◽

Black Swan ◽

Healthy Individuals ◽

X Ray ◽

Chest X Ray

AbstractThe COVID-19 pandemic in 2020 has highlighted the need to pull all available resources towards the mitigation of the devastating effects of such “Black Swan” events. Towards that end, we investigated the option to employ technology in order to assist the diagnosis of patients infected by the virus. As such, several state-of-the-art pre-trained convolutional neural networks were evaluated as of their ability to detect infected patients from chest X-Ray images. A dataset was created as a mix of publicly available X-ray images from patients with confirmed COVID-19 disease, common bacterial pneumonia and healthy individuals. To mitigate the small number of samples, we employed transfer learning, which transfers knowledge extracted by pre-trained models to the model to be trained. The experimental results demonstrate that the classification performance can reach an accuracy of 95% for the best two models.

Download Full-text

Expansion dataset COVID-19 chest X-ray using data augmentation and histogram equalization

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i2.pp1904-1909 ◽

2022 ◽

Vol 12 (2) ◽

pp. 1904

Author(s):

Farah Flayeh Alkhalid ◽

Abdulhakeem Qusay Albayati ◽

Ahmed Ali Alhammad

Keyword(s):

Data Augmentation ◽

State Of The Art ◽

Virus Disease ◽

Histogram Equalization ◽

Vital Role ◽

X Ray ◽

Current State ◽

Proposed Model ◽

Chest X Ray ◽

Using Data

The main important factor that plays vital role in success the deep learning is the deep training by many and many images, if neural networks are getting bigger and bigger but the training datasets are not, then it sounds like going to hit an accuracy wall. Briefly, this paper investigates the current state of the art of approaches used for a data augmentation for expansion the corona virus disease 2019 (COVID-19) chest X-ray images using different data augmentation methods (transformation and enhancement) the dataset expansion helps to rise numbers of images from 138 to 5520, the increasing rate is 3,900%, this proposed model can be used to expand any type of image dataset, in addition, the dataset have used with convolutional neural network (CNN) model to make classification if detected infection with COVID-19 in X-ray, the results have gotten high training accuracy=99%

Download Full-text

COVID-19 infection map generation and detection from chest X-ray images

Health Information Science and Systems ◽

10.1007/s13755-021-00146-8 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Aysen Degerli ◽

Mete Ahishali ◽

Mehmet Yamac ◽

Serkan Kiranyaz ◽

Muhammad E. H. Chowdhury ◽

...

Keyword(s):

State Of The Art ◽

Ground Truth ◽

Clinical Use ◽

X Ray ◽

Learning Techniques ◽

Map Generation ◽

Severity Grading ◽

Chest X Ray ◽

Novel Method ◽

Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

Download Full-text

Training Artificial Intelligence Neural Networks to Detect Devices on a Chest X-ray, a Pathway to COVID-19 Testing

Journal of the National Medical Association ◽

10.1016/j.jnma.2020.09.140 ◽

2020 ◽

Vol 112 (5) ◽

pp. S50

Author(s):

Zachary Eller ◽

Michelle Chen ◽

Jermaine Heath ◽

Uzma Hussain ◽

Thomas Obisean ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

X Ray ◽

Chest X Ray

Download Full-text

XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

New Generation Computing ◽

10.1007/s00354-021-00121-7 ◽

2021 ◽

Author(s):

Vishu Madaan ◽

Aditya Roy ◽

Charu Gupta ◽

Prateek Agrawal ◽

Anand Sharma ◽

...

Keyword(s):

Image Classification ◽

Second Phase ◽

Two Phase ◽

X Ray ◽

The Neural Network ◽

Rapid Spread ◽

Chest X Ray ◽

Polymerase Chain ◽

Two Phases ◽

Active Patients

AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

Download Full-text

COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks

11th Hellenic Conference on Artificial Intelligence ◽

10.1145/3411408.3411416 ◽

2020 ◽

Author(s):

Antonios Makris ◽

Ioannis Kontopoulos ◽

Konstantinos Tserpes

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

X Ray ◽

Chest X Ray

Download Full-text

Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

Mathematics ◽

10.3390/math9091002 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1002

Author(s):

Mohammad Khishe ◽

Fabio Caraffini ◽

Stefan Kuhn

Keyword(s):

Deep Learning ◽

Early Detection ◽

Iterative Process ◽

High Accuracy ◽

X Ray ◽

Starting Point ◽

Reliable Model ◽

Minimum Number ◽

Chest X Ray ◽

Deep Learning Model

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.

Download Full-text

Entropy estimation of the fragments of chest X-ray images

Кибернетика и программирование ◽

10.25136/2644-5522.2021.1.31676 ◽

2021 ◽

pp. 20-26

Author(s):

Aleksei Aleksandrovich Rumyantsev ◽

Farkhad Mansurovich Bikmuratov ◽

Nikolai Pavlovich Pashin

Keyword(s):

Neural Network ◽

Neural Networks ◽

Binary Classification ◽

Statistical Characteristics ◽

Type I ◽

Deep Convolutional Neural Networks ◽

Preliminary Calculation ◽

X Ray ◽

The Matrix ◽

Chest X Ray

The subject of this research is medical chest X-ray images. After fundamental pre-processing, the accumulated database of such images can be used for training deep convolutional neural networks that have become one of the most significant innovations in recent years. The trained network carries out preliminary binary classification of the incoming images and serve as an assistant to the radiotherapist. For this purpose, it is necessary to train the neural network to carefully minimize type I and type II errors. Possible approach towards improving the effectiveness of application of neural networks, by the criteria of reducing computational complexity and quality of image classification, is the auxiliary approaches: image pre-processing and preliminary calculation of entropy of the fragments. The article provides the algorithm for X-ray image pre-processing, its fragmentation, and calculation of the entropy of separate fragments. In the course of pre-processing, the region of lungs and spine is selected, which comprises approximately 30-40% of the entire image. Then the image is divided into the matrix of fragments, calculating the entropy of separate fragments in accordance with Shannon’s formula based pm the analysis of individual pixels. Determination of the rate of occurrence of each of the 255 colors allows calculating the total entropy. The use of entropy for detecting pathologies is based on the assumption that its values differ for separate fragments and overall picture of its distribution between the images with the norm and pathologies. The article analyzes the statistical values: standard deviation of error, dispersion. A fully connected neural network is used for determining the patterns in distribution of entropy and its statistical characteristics on various fragments of the chest X-ray image.

Download Full-text