Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks

Asif, Sohaib; Yi, Wenhui; Hou, Jin; Tao, Yi; Si, Jinhai

doi:10.1101/2020.05.01.20088211

Cited by 95 publications

(74 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They achieved 93.48% for the three-class performance. In comparison to these studies, the ResNet50V2 model in our proposed network (COV-MCNet) showed high accuracy than Ozturk et al [35] and Ioannis et al [37] and comparable accuracy with Asif and Wenhui [36].…”

Section: Performance Metricsmentioning

confidence: 48%

See 1 more Smart Citation

Automatic Classification Approach for Detecting COVID-19 using Deep Convolutional Neural Networks

Sarker¹,

Tan²,

Ma³

et al. 2020

Preprint

View full text Add to dashboard Cite

The COVID-19 pandemic situation has created even more difficulties in the quick identification and screening of the COVID-19 patients for the medical specialists. Therefore, a significant study is necessary for detecting COVID-19 cases using an automated diagnosis method, which can aid in controlling the spreading of the virus. In this paper, the study suggests a Deep Convolutional Neural Network-based multi-classification approach (COV-MCNet) using eight different pre-trained architectures such as VGG16, VGG19, ResNet50V2, DenseNet201, InceptionV3, MobileNet, InceptionResNetV2, Xception which are trained and tested on the X-ray images of COVID-19, Normal, Viral Pneumonia, and Bacterial Pneumonia. The results from 3-class (Normal vs. COVID-19 vs. Viral Pneumonia) showed that only the ResNet50V2 model provides the highest classification performance (accuracy: 95.83%, precision: 96.12%, recall: 96.11%, F1-score: 96.11%, specificity: 97.84%) compared to rest of the models. The results from 4-class (Normal vs. COVID-19 vs. Viral Pneumonia vs. Bacterial Pneumonia) demonstrated that the pre-trained model DenseNet201 provides the highest classification performance (accuracy: 92.54%, precision: 93.05%, recall: 92.81%, F1-score: 92.83%, specificity: 97.47%). Notably, the ResNet50V2 (3-class) and DenseNet201 (4-class) models in the proposed COV-MCNet framework showed higher accuracy compared to the rest six models. This indicates that the designed system can produce promising results to detect the COVID-19 cases on the availability of more data. The proposed multi-classification network (COV-MCNet) significantly speeds up the existing radiology-based method, which will be helpful to the medical community and clinical specialists for early diagnosis of the COVID-19 cases during this pandemic.

show abstract

Section: Performance Metricsmentioning

confidence: 48%

“…DarkNet achieved 87.02% accuracy for 3-class classification. Asif and Wenhui [36] proposed a transfer learning-based deep CNN model Inception V3 architecture to classify COVID-19 pneumonia and the study reported 96% accuracy. Ioannis et al [37] proposed a deep transfer learning-based classification task.…”

Section: Performance Metricsmentioning

confidence: 99%

Automatic Classification Approach for Detecting COVID-19 using Deep Convolutional Neural Networks

Sarker¹,

Tan²,

Ma³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, some researchers also use an adaptive winner filter for noise reduction [59], Affine Transformation [31] in their research. [2], [5], [6], [8], [10][11], [16], [18], [19], [20], [22], [23] [24],[ [26], [28], [ 30], [32], [37],43], [45], [46], [48], [50], [53], [54], [56], [60], [61], [63], [67] 31…”

Section: Generative Adversarial Network(gan)mentioning

confidence: 99%

“…Flipping or Rotating [1], [2][15], [16], [21], [22], [24], [25], [27], [35], [36], [37], [38], [42], [43], [4 6], [47], [48], [50], [51], [53], [55], [57], [61], [62], [65], [71], [74] 29…”

Section: Generative Adversarial Network(gan)mentioning

confidence: 99%

See 1 more Smart Citation

A Complete Survey on Automatically Diagnosing COVID-19 In the field of Computer Vision and A Collection of Medical Images

Panday¹

2020

Preprint

View full text Add to dashboard Cite

As COVID-19 is the source of millions of deaths throughout the world, it turned obligatory to fight against the COVID-19 pandemic. Due to the need for expensive equipment, experienced radiologists, and the time-consuming in Reverse Transcription Polymerase Chain Reaction (RT-PCR) test, researchers find out the necessity to embrace X-ray images and Computed Tomography (CT) images based diagnosing. Wreak havoc of COVID-19 instigated me to review current emerging Artificial Intelligence(AI) based automatic diagnosing models through the statistical survey that will pave out the way of research. In this paper, I study different available research resources at the time span from April 2020 to July 2020. In order to help researchers in further research, I presented a statistical survey so that researchers can pick a preeminent diagnosing model. I took a look at 74 papers from April to July and specified preprocessing techniques, feature extraction, classification method, interpretability method, and experimental result. Moreover, I analyze training,testing and validation split ratio, as well as look into the dataset’s availability publicly. Some researchers are able to gain noticeable performance by adopting their own local model. On the contrary, some researchers adopt an existing pre-trained model and achieve the utmost result. Some models need to feed huge data and some models outperform despite having small data. In the following sections, all of the criteria will be illustrated briefly.

show abstract

A deep and handcrafted features‐based framework for diagnosis of COVID‐19 from chest x‐ray images

Bozkurt

2021

Concurrency and Computation

View full text Add to dashboard Cite

Automatic early diagnosis of COVID‐19 with computer‐aided tools is crucial for disease treatment and control. Radiology images of COVID‐19 and other lung diseases like bacterial pneumonia, viral pneumonia have common features. Thus, this similarity makes it difficult for radiologists to detect COVID‐19 cases. A reliable method for classifying non‐COVID‐19 and COVID‐19 chest x‐ray images could be useful to reduce triage process and diagnose. In this study, we develop an original framework (HANDEFU) that supports handcrafted, deep, and fusion‐based feature extraction techniques for feature engineering. The user interactively builds any model by selecting feature extraction technique and classification method through the framework. Any feature extraction technique and model could then be added dynamically to the library of software at a later time upon request. The novelty of this study is that image preprocessing and diverse feature extraction and classification techniques are assembled under an original framework. In this study, this framework is utilized for diagnosing COVID‐19 from chest x‐ray images on an open‐access dataset. All of the experimental results and performance evaluations on this dataset are performed with this software. In experimental studies, COVID‐19 prediction is performed by 27 different models through software. The superior performance with accuracy of 99.36% is obtained by LBP+SVM model.

show abstract

Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks

Cited by 95 publications

References 34 publications

Automatic Classification Approach for Detecting COVID-19 using Deep Convolutional Neural Networks

Automatic Classification Approach for Detecting COVID-19 using Deep Convolutional Neural Networks

A Complete Survey on Automatically Diagnosing COVID-19 In the field of Computer Vision and A Collection of Medical Images

A deep and handcrafted features‐based framework for diagnosis of COVID‐19 from chest x‐ray images

Contact Info

Product

Resources

About