A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-Ray images

Barshooi, Amir Hossein; Amirkhani, Abdollah

doi:10.1016/j.bspc.2021.103326

Cited by 70 publications

(36 citation statements)

References 47 publications

(37 reference statements)

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“…Local perception is an efficient detection method that is currently attracting attention. This method mainly detects the local aspect of data to extract the basic features for the visual object in a picture, such as an angle or arc of an animal [ 15 ]. The advantage of CNN is the requirement of a few parameters compared with the number of hidden units for fully connected networks.…”

Section: Methodsmentioning

confidence: 99%

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Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Abdulkareem

Mostafa

Al-Qudsy

et al. 2022

Journal of Healthcare Engineering

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Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.

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Section: Methodsmentioning

confidence: 99%

“…Many studies have presented the influence and strength of these techniques in image segmentation [ 13 ]. Image classification [ 14 ] and image segmentation [ 13 ] for medical imaging has also produced very good results using CNN architectures [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Abdulkareem

Mostafa

Al-Qudsy

et al. 2022

Journal of Healthcare Engineering

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“…Since one of the main drawbacks of DL is the requirement for a large pool of data [61] , augmentation procedures have become common practice to improve the generalization and the accuracy of models [62] . Successful results in augmenting biometric data for the detection of COVID-19 were obtained by Barshooi and Amirkani [63] with a novel approach based on synthetic GAN-generated data, pre-processed by a Gabor filter. However, the images fed to our CNN are in fact time plots, and most graphical artifacts and/or synthesis methods would result in unrealistic augmented data which would bring in the risk of biasing the net.…”

Section: Methodsmentioning

confidence: 99%

Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures

Costantini

Cesarini

Robotti

et al. 2022

Knowledge-Based Systems

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“…Their model achieved a test accuracy of 97.4% between the used models. Barshooi et al [23] proposed a model for screening for COVID-19 infection using a GAN DL approach. Data augmentation were employed using different filter banks.…”

Section: Related Workmentioning

confidence: 99%

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

et al. 2022

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Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated using different activation functions and thresholds with different optimizers. We used a benchmark dataset from the kaggle website. The proposed model has been evaluated on 7395 images that consist of 3 classes (COVID-19, normal and pneumonia). Additionally, we compared our framework with the traditional pre-trained deep learning models and with other literature studies. Our evaluation using various metrics showed that our framework achieved a high test accuracy of 99.3% with a minimum loss of 0.02 using the LeakyReLU activation function at a threshold equal to 0.1 with the RMSprop optimizer. Additionally, we achieved a sensitivity and specificity of 99 and F1-Score of 99.3% with only 10 epochs and a 10−4 learning rate.

show abstract

A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-Ray images

Cited by 70 publications

References 47 publications

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

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