An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network

Baghdadi, Nadiah A.; Malki, Amer; Abdelaliem, Sally F; Balaha, Hossam Magdy; Badawy, Mahmoud; Elhosseini, Mostafa A.

doi:10.1016/j.compbiomed.2022.105383

Cited by 70 publications

(42 citation statements)

References 26 publications

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“…The last column defines a keyword to be used in the experiments instead of using the whole model name. To evaluate the model, different performance metrics are calculated ( Seliya, Khoshgoftaar & Van Hulse, 2009 ; Baghdadi et al, 2022b , 2022a ): (1) accuracy ( Eq. (1) ), (2) balanced accuracy ( Eq.…”

Section: Methodsmentioning

confidence: 99%

An optimized deep learning approach for suicide detection through Arabic tweets

Baghdadi

Malki

Balaha

et al. 2022

PeerJ Computer Science

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Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including this one, suggest using social media data to track depression and other mental illnesses. Even though Arabic is widely spoken and has a complex syntax, depressive detection methods have not been applied to the language. The Arabic tweets dataset should be scraped and annotated first. Then, a complete framework for categorizing tweet inputs into two classes (such as Normal or Suicide) is suggested in this study. The article also proposes an Arabic tweet preprocessing algorithm that contrasts lemmatization, stemming, and various lexical analysis methods. Experiments are conducted using Twitter data scraped from the Internet. Five different annotators have annotated the data. Performance metrics are reported on the suggested dataset using the latest Bidirectional Encoder Representations from Transformers (BERT) and Universal Sentence Encoder (USE) models. The measured performance metrics are balanced accuracy, specificity, F1-score, IoU, ROC, Youden Index, NPV, and weighted sum metric (WSM). Regarding USE models, the best-weighted sum metric (WSM) is 80.2%, and with regards to Arabic BERT models, the best WSM is 95.26%.

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Section: Methodsmentioning

confidence: 99%

An optimized deep learning approach for suicide detection through Arabic tweets

Baghdadi

Malki

Balaha

et al. 2022

PeerJ Computer Science

Self Cite

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“…[ 36 ] propose a classification method of COVID-19 based on CT images. The method integrated CNN with transfer learning and sparrow search algorithm (SpaSA) for hyperparameter optimization.…”

Section: Related Workmentioning

confidence: 99%

“…ML-based models that used radiography can produce more accurate and reliable results [35]. [36] propose a classification method of COVID-19 based on CT images. e method integrated CNN with transfer learning and sparrow search algorithm (SpaSA) for hyperparameter optimization.…”

Section: Related Workmentioning

confidence: 99%

Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model

Mohammed

Al‐Khateeb

Yousif

et al. 2022

Computational Intelligence and Neuroscience

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Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues. This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis. The crow swarm optimization is employed to find an optimal set of coefficients using a designed fitness function for evaluating the performance of the deep learning models. The crow swarm optimization is modified to obtain a good selected coefficient distribution by considering the best average fitness. We have utilized two datasets: the first dataset includes 746 computed tomography images, 349 of them are of confirmed COVID-19 cases and the other 397 are of healthy individuals, and the second dataset are composed of unimproved computed tomography images of the lung for 632 positive cases of COVID-19 with 15 trained and pretrained deep learning models with nine evaluation metrics are used to evaluate the developed methodology. Among the pretrained CNN and deep models using the first dataset, ResNet50 has an accuracy of 91.46% and a F1-score of 90.49%. For the first dataset, the ResNet50 algorithm is the optimal deep learning model selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5715.988 for COVID-19 computed tomography lung images case considered differential advancement. In contrast, the VGG16 algorithm is the optimal deep learning model is selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5758.791 for the second dataset. Overall, InceptionV3 had the lowest performance for both datasets. The proposed evaluation methodology is a helpful tool to assist healthcare managers in selecting and evaluating the optimal COVID-19 diagnosis models based on deep learning.

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“…While classification involves assigning the image as a single pattern to a predefined class, the SS involves a pixel‐level classification task. Previous works commonly focused on identifying COVID‐19 at the lung level, 30 , 31 but pixel‐wise segmentation of pulmonary infections was relatively limited.…”

Section: Related Workmentioning

confidence: 99%

A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID‐19 lung infections

Polat

2022

Int J Imaging Syst Tech

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Coronavirus disease (COVID‐19) affects the lives of billions of people worldwide and has destructive impacts on daily life routines, the global economy, and public health. Early diagnosis and quantification of COVID‐19 infection have a vital role in improving treatment outcomes and interrupting transmission. For this purpose, advances in medical imaging techniques like computed tomography (CT) scans offer great potential as an alternative to RT‐PCR assay. CT scans enable a better understanding of infection morphology and tracking of lesion boundaries. Since manual analysis of CT can be extremely tedious and time‐consuming, robust automated image segmentation is necessary for clinical diagnosis and decision support. This paper proposes an efficient segmentation framework based on the modified DeepLabV3+ using lower atrous rates in the Atrous Spatial Pyramid Pooling (ASPP) module. The lower atrous rates make receptive small to capture intricate morphological details. The encoder part of the framework utilizes a pre‐trained residual network based on dilated convolutions for optimum resolution of feature maps. In order to evaluate the robustness of the modified model, a comprehensive comparison with other state‐of‐the‐art segmentation methods was also performed. The experiments were carried out using a fivefold cross‐validation technique on a publicly available database containing 100 single‐slice CT scans from >40 patients with COVID‐19. The modified DeepLabV3+ achieved good segmentation performance using around 43.9 M parameters. The lower atrous rates in the ASPP module improved segmentation performance. After fivefold cross‐validation, the framework achieved an overall Dice similarity coefficient score of 0.881. The results demonstrate that several minor modifications to the DeepLabV3+ pipeline can provide robust solutions for improving segmentation performance and hardware implementation.

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An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network

Cited by 70 publications

References 26 publications

An optimized deep learning approach for suicide detection through Arabic tweets

An optimized deep learning approach for suicide detection through Arabic tweets

Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model

A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID‐19 lung infections

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