A Fine-tuned deep convolutional neural network for chest radiography image classification on COVID-19 cases

Dash, Amiya Kumar; Mohapatra, Puspanjali

doi:10.1007/s11042-021-11388-9

Cited by 14 publications

(4 citation statements)

References 23 publications

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“…Fine-tuning is a process that takes a model that has already been trained (pre-trained) for one task and returns it or tweaks the same model to perform a classification task [ 43 , 44 ]. Assuming that the original and new tasks are similar, using an artificial neural network that has already been designed and trained allows us to leverage what the model has already learned rather than developing it from scratch [ 45 , 46 ].…”

Section: The Proposed Approachmentioning

confidence: 99%

A Hybrid Deep Learning Model for Brain Tumour Classification

Rasool

Ismail

Boulila

et al. 2022

Entropy

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A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.

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Section: The Proposed Approachmentioning

confidence: 99%

A Hybrid Deep Learning Model for Brain Tumour Classification

Rasool

Ismail

Boulila

et al. 2022

Entropy

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“…Several researchers focused on COVID and non-COVID binary classification [ 24 – 28 ]. However, many researchers have worked on three-class classification (normal, COVID-19, and pneumonia) as well [ 7 , 29 – 34 ]. However, fewer focused on four-class classification (normal, COVID-19 pneumonia, bacterial pneumonia, and viral pneumonia) [ 6 , 35 ].…”

Section: Literature Surveymentioning

confidence: 99%

“…A dataset of 2905 X-ray images was used for the training and validation of the model. In the research study [ 29 ], Dash et al used a fine-tuned pre-trained model with chest radiography images. They proposed a unique framework by removing fully connected layers of VGG-16 and placing a new simplified, fully connected layer assigned with random weight.…”

Section: Literature Surveymentioning

confidence: 99%

Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images

Ahmad

Bajwa

Mehmood

et al. 2023

Neural Comput & Applic

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The new COVID-19 emerged in a town in China named Wuhan in December 2019, and since then, this deadly virus has infected 324 million people worldwide and caused 5.53 million deaths by January 2022. Because of the rapid spread of this pandemic, different countries are facing the problem of a shortage of resources, such as medical test kits and ventilators, as the number of cases increased uncontrollably. Therefore, developing a readily available, low-priced, and automated approach for COVID-19 identification is the need of the hour. The proposed study uses chest radiography images (CRIs) such as X-rays and computed tomography (CTs) to detect chest infections, as these modalities contain important information about chest infections. This research introduces a novel hybrid deep learning model named Lightweight ResGRU that uses residual blocks and a bidirectional gated recurrent unit to diagnose non-COVID and COVID-19 infections using pre-processed CRIs. Lightweight ResGRU is used for multi-modal two-class classification (normal and COVID-19), three-class classification (normal, COVID-19, and viral pneumonia), four-class classification (normal, COVID-19, viral pneumonia, and bacterial pneumonia), and COVID-19 severity types’ classification (i.e., atypical appearance, indeterminate appearance, typical appearance, and negative for pneumonia). The proposed architecture achieved f-measure of 99.0%, 98.4%, 91.0%, and 80.5% for two-class, three-class, four-class, and COVID-19 severity level classifications, respectively, on unseen data. A large dataset is created by combining and changing different publicly available datasets. The results prove that radiologists can adopt this method to screen chest infections where test kits are limited.

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“…The necessity for faster turnaround times to interpret radiographic images has led to a substantial effort to adopt CNN-based techniques, with a concentrated effort on distinguishing COVID-19 infected patients with the aid of both CT [ [15] , [16] , [17] , [18] , [19] , [20] , [21] ] and X-ray [ 14 , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] ] imaging. Several overviews into the application of CNN techniques to aid in COVID-19 diagnosis have been conducted and we refer the reader to Refs.…”

Section: Related Workmentioning

confidence: 99%

Indirect supervision applied to COVID-19 and pneumonia classification

Danilov

Proutski²,

Karpovsky

et al. 2022

Informatics in Medicine Unlocked

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The novel coronavirus 19 (COVID-19) continues to have a devastating effect around the globe, leading many scientists and clinicians to actively seek to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the healthcare industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a CNN-based method for the detection of COVID-19 by utilizing patients' chest X-ray images. Developing upon the inclusion of convolutional units, the proposed method makes use of indirect supervision based on Grad-CAM. This technique is used in the training process where Grad-CAM's attention heatmaps support the network's predictions. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combining publicly available data across 5 different sources and carefully annotate the comprising images across three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on indirect supervision of traditional classification networks, where the guidance is directed by an external algorithm. With this method, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models, specifically for COVID-19, with one network in particular, VGG-16, outperforming the best of the tailor-made models.

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A Fine-tuned deep convolutional neural network for chest radiography image classification on COVID-19 cases

Cited by 14 publications

References 23 publications

A Hybrid Deep Learning Model for Brain Tumour Classification

A Hybrid Deep Learning Model for Brain Tumour Classification

Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images

Indirect supervision applied to COVID-19 and pneumonia classification

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