Classification of COVID-19 and Pneumonia Using Deep Transfer Learning

Mahin, Mainuzzaman; Tonmoy, Sajid; Islam, Rufaed; Tazin, Tahia; Khan, Mohammad Monirujjaman; Bourouis, Sami

doi:10.1155/2021/3514821

Cited by 16 publications

(12 citation statements)

References 51 publications

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“…The proposed technique attained a classification accuracy of 89.2%. Mahin et al [35] presented a technique for binary classification of chest X-rays using a transfer learning approach. The technique utilizes three pretrained deep learning models that are fine-tuned for binary chest X-ray classification.…”

Section: Related Workmentioning

confidence: 99%

An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

Khan¹,

Rehman²,

Ahmed³

et al. 2023

Computer Systems Science and Engineering

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In 2020, COVID-19 started spreading throughout the world. This deadly infection was identified as a virus that may affect the lungs and, in severe cases, could be the cause of death. The polymerase chain reaction (PCR) test is commonly used to detect this virus through the nasal passage or throat. However, the PCR test exposes health workers to this deadly virus. To limit human exposure while detecting COVID-19, image processing techniques using deep learning have been successfully applied. In this paper, a strategy based on deep learning is employed to classify the COVID-19 virus. To extract features, two deep learning models have been used, the DenseNet201 and the SqueezeNet. Transfer learning is used in feature extraction, and models are fine-tuned. A publicly available computerized tomography (CT) scan dataset has been used in this study. The extracted features from the deep learning models are optimized using the Ant Colony Optimization algorithm. The proposed technique is validated through multiple evaluation parameters. Several classifiers have been employed to classify the optimized features. The cubic support vector machine (Cubic SVM) classifier shows superiority over other commonly used classifiers and attained an accuracy of 98.72%. The proposed technique achieves state-of-the-art accuracy, a sensitivity of 98.80%, and a specificity of 96.64%.

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Section: Related Workmentioning

confidence: 99%

An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

Khan¹,

Rehman²,

Ahmed³

et al. 2023

Computer Systems Science and Engineering

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“…It is frequently used to build customized deep-learning models to support smaller datasets [5] . These models sometimes perform better than fully trained networks, where fine-tuning helps achieve better performance [6] . This paper will show how transfer learning detects COVID-19 in CT scan images.…”

Section: Introductionmentioning

confidence: 99%

A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications

Kathamuthu¹,

Shanthi²,

et al. 2023

Advances in Engineering Software

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“…Recent research on this subject has focused on the use of artificial intelligence (AI) to aid the diagnosis of respiratory diseases (4)(5)(6)(7)(8), especially the diagnosis of coronavirus disease 2019 . However, most studies investigated intelligent diagnosis assistance during the formal diagnostic phase when medical examinations have been performed.…”

Section: Introductionmentioning

confidence: 99%

An intelligent prediagnosis system for disease prediction and examination recommendation based on electronic medical record and a medical-semantic-aware convolution neural network (MSCNN) for pediatric chronic cough

Zhu¹,

Li²,

Huang³

et al. 2022

Transl Pediatr

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Background Due to the phenotypic similarities among different pediatric respiratory diseases with chronic cough, primary doctors often misdiagnose and the misuse of examinations is prevalent. In the pre-diagnosis stage, the patients' chief complaints and other information in the electronic medical record (EMR) provide a powerful reference for respiratory experts to make preliminary disease judgment and examination plan. In this paper, we proposed an intelligent prediagnosis system to predict disease diagnosis and recommend examinations based on EMR text. Methods We examined the clinical notes of 178,293 children with chronic cough symptoms from retrospective EMR data. The dataset is split into 7:3 for training and testing. From the testing set, we also extract 5% of samples for validation. We proposed a medical-semantic-aware convolution neural network (MSCNN) framework that can accomplish two downstream tasks from the same medical language model through transfer learning. First, a medical language model based on the word2vec algorithm was built to generate embeddings for the text data. Then, text convolutional neural network (TextCNN) was used to build models for disease prediction and examination recommendation. Results We implemented 5 algorithms for disease prediction. In the disease prediction task, our algorithm outperformed the baseline methods on all metrics, with a top-1 accuracy (AC) of 0.68 and a top-3 AC of 0.923 on the testing set. By adding data enhancement, the top-3 AC reached 0.926. In the examination recommendation task, the overall AC on the testing set was 0.93 and the macro average (MA) F1-score was 0.88. The average area under the curve (AUC) on the training set was 0.97 while on the testing set it was 0.86. Conclusions We constructed an intelligent prediagnosis system with an MSCNN framework that can predict diseases and make examination recommendations based on EMR data. Our approach achieved good results on a retrospective clinical dataset and thus has great potential for the application of automated diagnosis assist in clinical practice during pre-diagnosis stage, which will provide help for primary level doctors or doctors in basic-level hospitals. Due to the generality of the proposed framework, it can be straight forwardly extended to prediagnosis for other diseases.

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Classification of COVID-19 and Pneumonia Using Deep Transfer Learning

Cited by 16 publications

References 51 publications

An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications

An intelligent prediagnosis system for disease prediction and examination recommendation based on electronic medical record and a medical-semantic-aware convolution neural network (MSCNN) for pediatric chronic cough

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