A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images

Joshi, Rakesh Chandra; Yadav, Saumya; Pathak, Vinay Kumar; Malhotra, Hardeep Singh; Khokhar, Harsh Vardhan; Parihar, Anit; Kohli, Neera; Himanshu, D; Garg, Richa; Bhatt, Madan Lal Brahma; Kumar, Raj; Singh, Naresh Pal; Sardana, Vijay; Bürget, Radim; Alippi, Cesare; González, Carmelo Javier León; Dutta, Maitreyee

doi:10.1016/j.bbe.2021.01.002

Cited by 50 publications

(42 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some models are suitable for 2-class classification, and some models are suitable for multiclass classification. Hence, the model MCFF-Net66-Conv1-GAP is compared with the methods of Khan [ 17 ], Hussain [ 19 ], Mangal [ 34 ], and Joshi [ 35 ]. The comparison results are shown in Table 3 .…”

Section: Experiments and Resultsmentioning

confidence: 99%

Detecting COVID‐19 in Chest X‐Ray Images via MCFF‐Net

Wang

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). Due to the rapid spread of COVID-19 around the world, the number of COVID-19 cases continues to increase, and lots of countries are facing tremendous pressure on both public and medical resources. Although RT-PCR is the most widely used detection technology with COVID-19 detection, it still has some limitations, such as high cost, being time-consuming, and having low sensitivity. According to the characteristics of chest X-ray (CXR) images, we design the Parallel Channel Attention Feature Fusion Module (PCAF), as well as a new structure of convolutional neural network MCFF-Net proposed based on PCAF. In order to improve the recognition efficiency, the network adopts 3 classifiers: 1-FC, GAP-FC, and Conv1-GAP. The experimental results show that the overall accuracy of MCFF-Net66-Conv1-GAP model is 94.66% for 4-class classification. Simultaneously, the classification accuracy, precision, sensitivity, specificity, and F1-score of COVID-19 are 100%. MCFF-Net may not only assist clinicians in making appropriate decisions for COVID-19 diagnosis but also mitigate the lack of testing kits.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Detecting COVID‐19 in Chest X‐Ray Images via MCFF‐Net

Wang

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Images from CT scans offer more accurate details than X-rays. In various works reported in the survey, for pneumonia, lung cancer, and COVID-19 recognition, CT scans had utilized to diagnose lung disease [4], [27].…”

Section: Deep Learning Methodologymentioning

confidence: 99%

Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey

Ahmed

Kadhem

2021

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

<p class="0abstract"><strong>—</strong> Chest imaging diagnostics is crucial in the medical area due to many serious lung diseases like cancers and nodules and particularly with the current pandemic of Covid-19. Machine learning approaches yield prominent results toward the task of diagnosis. Recently, deep learning methods are utilized and recommended by many studies in this domain. The research aims to critically examine the newest lung disease detection procedures using deep learning algorithms that use X-ray and CT scan datasets. Here, the most recent studies in this area (2015-2021) have been reviewed and summarized to provide an overview of the most appropriate methods that should be used or developed in future works, what limitations should be considered, and at what level these techniques help physicians in identifying the disease with better accuracy. The lack of various standard datasets, the huge training set, the high dimensionality of data, and the independence of features have been the main limitations based on the literature. However, different architectures of deep learning are used by many researchers but, Convolutional Neural Networks (CNN) are still state-of-art techniques in dealing with image datasets.</p>

show abstract

“…In other words, ground-glass opacities that are not visible in the X-ray image can be seen when imaging with CT. Some of the studies in the literature were performed on CT images [15][16][17][18][19], some on X-ray images [20][21][22][23][24], and some on both [25][26][27]. In our experimental studies, we use two different CT data sets and a mixed data set consisting of combining these data sets.…”

Section: Related Workmentioning

confidence: 99%

Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier

Saygılı

2021

Arab J Sci Eng

View full text Add to dashboard Cite

COVID-19 is a virus that has been declared an epidemic by the world health organization and causes more than 2 million deaths in the world. To achieve this, computer-aided automatic diagnosis systems are created on medical images. In this study, an image processing and machine learning-based method is proposed that enables segmenting of CT images taken from COVID-19 patients and automatic detection of the virus through the segmented images. The main purpose of the study is to automatically diagnose the COVID-19 virus. The study consists of three basic steps: preprocessing, segmentation and classification. Image resizing, image sharpening, noise removal, contrast stretching processes are included in the preprocessing phase and segmentation of images with Expectation-Maximization-based Gaussian Mixture Model in the segmentation phase. In the classification stage, COVID-19 is classified as positive and negative by using kNN, decision tree, and two different ensemble methods together with the kernel support vector machines method. In the study, two different CT datasets that are open to the public and a mixed dataset created by combining these datasets were used. The best accuracy values for Dataset-1, Dataset-2 and Mixed Dataset are 98.5%, 86.3%, 94.5%, respectively. The achieved results prove that the proposed approach advances state-of-the-art performance. Within the scope of the study, a GUI that can automatically detect COVID-19 has been created.

show abstract

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images

Cited by 50 publications

References 40 publications

Detecting COVID‐19 in Chest X‐Ray Images via MCFF‐Net

Detecting COVID‐19 in Chest X‐Ray Images via MCFF‐Net

Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey

Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier

Contact Info

Product

Resources

About