A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

Anis, Shazia; Xuan, Tan Zi; Chuah, Joon Huang; Usman, Juliana; Qian, Pengjiang; Lai, Khin Wee

doi:10.1186/s13634-021-00755-1

Cited by 52 publications

(20 citation statements)

References 31 publications

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“…The work carried out by Shazia et al [15] states that applying deep learning techniques to radiological images for novel coronavirus identification has the potential to reduce the workload of medical practitioners and increase the accuracy and efficiency of COVID-19 diagnosis. In what follows, we discuss the reviewed articles on developing DL models for the early diagnosis of COVID-19 using medical images, and attempt to answer these research questions: What are the most commonly used DL techniques for COVID-19 detection using medical images?…”

Section: Discussionmentioning

confidence: 99%

Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging

Moghaddam

Ghavipour

2022

IPEM-Translation

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Section: Discussionmentioning

confidence: 99%

Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging

Moghaddam

Ghavipour

2022

IPEM-Translation

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“…However, the parameters employed in the analysis of such work are huge, actually millions [17]. For example, GoogleNet-V1 has about 5 million [31], ResNet-50 has about 25 million [32], AlexNET has about 60 million [33], and VGG-16 has about 138 million parameters [34,35]. Consequently, the time required for training and testing is also large (in the range of thousands of seconds), even with the use of multiple graphics processing units (GPUs).…”

Section: Discussionmentioning

confidence: 99%

Fast COVID-19 Detection from Chest X-Ray Images Using DCT Compression

Taher

Haweel

Bastaki

et al. 2022

Applied Computational Intelligence and Soft Computing

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Novel coronavirus (COVID-19) is a new strain of coronavirus, first identified in a cluster with pneumonia symptoms caused by SARS-CoV-2 virus. It is fast spreading all over the world. Most infected people will develop mild to moderate illness and recover without hospitalization. Currently, real-time quantitative reverse transcription-PCR (rqRT-PCR) is popular for coronavirus detection due to its high specificity, simple quantitative analysis, and higher sensitivity than conventional RT-PCR. Antigen tests are also commonly used. It is very essential for the automatic detection of COVID-19 from publicly available resources. Chest X-ray (CXR) images are used for the classification of COVID-19, normal, and viral pneumonia cases. The CXR images are divided into sub-blocks for finding out the discrete cosine transform (DCT) for every sub-block in this proposed method. In order to produce a compressed version for each CXR image, the DCT energy compaction capability is used. For each image, hardly few spectral DCT components are included as features. The dimension of the final feature vectors is reduced by scanning the compressed images using average pooling windows. In the 3-set classification, a multilayer artificial neural network is used. It is essential to triage non-COVID-19 patients with pneumonia to give out hospital resources efficiently. Higher size feature vectors are used for designing binary classification for COVID-19 and pneumonia. The proposed method achieved an average accuracy of 95% and 94% for the 3-set classification and binary classification, respectively. The proposed method achieves better accuracy than that of the recent state-of-the-art techniques. Also, the time required for the implementation is less.

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“…ResNet50 consists of 50 layers (48 convolutional layers, 1 average pool layer, and 1 max pool layer) that contain approximately 23 million parameters [ 36 ]. The model is widely used in computer vision problems due to the core advantage of the architecture, which helps to reduce the vanishing gradient problems by following the alternate shortcut path.…”

Section: Methodsmentioning

confidence: 99%

A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images

et al. 2022

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Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.

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A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

Cited by 52 publications

References 31 publications

Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging

Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging

Fast COVID-19 Detection from Chest X-Ray Images Using DCT Compression

A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images

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