Deep convolutional approaches for the analysis of Covid-19 using chest X-Ray images from portable devices

Moura, Joaquim de; Ramos, Lucía; Vidal, Plácido L.; Cruz, Milena; Abelairas, Laura; Castro, Eva Martínez de; Novo, Jorge

doi:10.1101/2020.06.18.20134593

Cited by 9 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [257] two complementary deep learning approaches based on densely convolutional network architecture are proposed. The joint response of the two approaches enhances the performance of the individual methods.…”

Section: Chest Computed Tomography and X-ray Image Processingmentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

151

View full text Add to dashboard Cite

show abstract

Section: Chest Computed Tomography and X-ray Image Processingmentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

151

View full text Add to dashboard Cite

show abstract

“…In 2020, De et al [8] have proposed a unique automatic method for the detection of chest X-ray images developed by handy device into two various clinical categories such as pathological and normal. There were three deep learning methods based on densely CNN architecture.…”

Section: Related Workmentioning

confidence: 99%

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

Das¹

2021

Multimed Tools Appl

View full text Add to dashboard Cite

COVID-19 disease is a major health calamity in twentieth century, in which the infection is spreading at the global level. Developing countries like Bangladesh, India, and others are still facing a delay in recognizing COVID-19 cases. Hence, there is a need for immediate recognition with perfect identification of infection. This clear visualization helps to save the life of suspected COVID-19 patients. With the help of traditional RT-PCR testing, the combination of medical images and deep learning classifiers delivers more hopeful results with high accuracy in the prediction and recognition of COVID-19 cases. COVID-19 disease is recently researched through sample chest X-ray images, which have already proven its efficiency in lung diseases. To emphasize corona virus testing methods and to control the community spreading, the automatic detection process of COVID-19 is processed through the detailed medication reports from medical images. Although there are numerous challenges in the manual understanding of traces in COVID-19 infection from X-ray, the subtle differences among normal and infected X-rays can be traced by the data patterns of Convolutional Neural Network (CNN). To improve the detection performance of CNN, this paper plans to develop an Ensemble Learning with CNN-based Deep Features (EL-CNN-DF). In the initial phase, image scaling and median filtering perform the pre-processing of the chest X-ray images gathered from the benchmark source. The second phase is lung segmentation, which is the significant step for COVID detection. It is accomplished by the Adaptive Activation Function-based U-Net (AAF-U-Net). Once the lungs are segmented, it is subjected to novel EL-CNN-DF, in which the deep features are extracted from the pooling layer of CNN, and the fully connected layer of CNN are replaced with the three classifiers termed “Support Vector Machine (SVM), Autoencoder, Naive Bayes (NB)”. The final detection of COVID-19 is done by these classifiers, in which high ranking strategy is utilized. As a modification, a Self Adaptive-Tunicate Swarm Algorithm (SA-TSA) is adopted as a boosting algorithm to enhance the performance of segmentation and detection. The overall analysis has shown that the precision of the enhanced CNN by using SA-TSA was 1.02%, 4.63%, 3.38%, 1.62%, 1.51% and 1.04% better than SVM, autoencoder, NB, Ensemble, RNN and LSTM respectively. The comparative performance analysis on existing model proves that the proposed algorithm is better than other algorithms in terms of segmentation and classification of COVID-19 detection.

show abstract

“…12,13 To overcome this issue, the artificial intelligence (AI) community has presented numerous machine -and deep learning (DL)-based image analysis tools that are able to automatically differentiate between COVID-19 positive and negative patients based on a single CXR, with reported accuracies and sensitivities often well over 90%. [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] One of the first of such networks was COVID-Net, reaching 93.3% accuracy on the test set of their publicly available dataset termed COVIDx. 34 As large single hospital CXR datasets of both COVID-19 positive and negative patients are scarce, researchers looking into these DL methods have often made use of a combination of publicly available repositories.…”

Section: Introductionmentioning

confidence: 99%

“…CXR for COVID‐19 diagnosis however still requires expert radiologists (>10 years of experience) to interpret the images with high specificity, a bottleneck in the workflow that is both time consuming and costly 12,13 . To overcome this issue, the artificial intelligence (AI) community has presented numerous machine – and deep learning (DL)‐based image analysis tools that are able to automatically differentiate between COVID‐19 positive and negative patients based on a single CXR, with reported accuracies and sensitivities often well over 90% 14–33 . One of the first of such networks was COVID‐Net, reaching 93.3% accuracy on the test set of their publicly available dataset termed COVIDx 34 …”

Section: Introductionmentioning

confidence: 99%

Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning – Success story or dataset bias?

2022

View full text Add to dashboard Cite

Purpose: Over the last 2 years, the artificial intelligence (AI) community has presented several automatic screening tools for coronavirus disease 2019 based on chest radiography (CXR), with reported accuracies often well over 90%. However, it has been noted that many of these studies have likely suffered from dataset bias, leading to overly optimistic results. The purpose of this study was to thoroughly investigate to what extent biases have influenced the performance of a range of previously proposed and promising convolutional neural networks (CNNs), and to determine what performance can be expected with current CNNs on a realistic and unbiased dataset. Methods: Five CNNs for COVID-19 positive/negative classification were implemented for evaluation, namely VGG19, ResNet50, InceptionV3, DenseNet201, and COVID-Net. To perform both internal and cross-dataset evaluations, four datasets were created. The first dataset Valencian Region Medical Image Bank (BIMCV) followed strict reverse transcriptase-polymerase chain reaction (RT-PCR) test criteria and was created from a single reliable open access databank, while the second dataset (COVIDxB8) was created through a combination of six online CXR repositories. The third and fourth datasets were created by combining the opposing classes from the BIMCV and COVIDxB8 datasets. To decrease inter-dataset variability, a pre-processing workflow of resizing, normalization, and histogram equalization were applied to all datasets. Classification performance was evaluated on unseen test sets using precision and recall. A qualitative sanity check was performed by evaluating saliency maps displaying the top 5%, 10%, and 20% most salient segments in the input CXRs, to evaluate whether the CNNs were using relevant information for decision making. In an additional experiment and to further investigate the origin of potential dataset bias, all pixel values outside the lungs were set to zero through automatic lung segmentation before training and testing. Results: When trained and evaluated on the single online source dataset (BIMCV), the performance of all CNNs is relatively low (precision: 0.65-0.72, recall: 0.59-0.71), but remains relatively consistent during external evaluation (precision: 0.58-0.82, recall: 0.57-0.72). On the contrary, when trained and internally evaluated on the combinatory datasets, all CNNs performed well across all metrics (precision: 0.94-1.00, recall: 0.77-1.00). However, when subsequently evaluated cross-dataset, results dropped substantially (precision: 0.10-0.61, recall: 0.04-0.80). For all datasets, saliency maps revealed the CNNs rarelyThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Deep convolutional approaches for the analysis of Covid-19 using chest X-Ray images from portable devices

Cited by 9 publications

References 27 publications

Applications of artificial intelligence in battling against covid-19: A literature review

Applications of artificial intelligence in battling against covid-19: A literature review

Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis

Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning – Success story or dataset bias?

Contact Info

Product

Resources

About