Hybrid deep learning for detecting lung diseases from X-ray images

Bharati, Subrato; Podder, Prajoy; Mondal, M. Rubaiyat Hossain

doi:10.1016/j.imu.2020.100391

Cited by 293 publications

(140 citation statements)

References 27 publications

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“…Such identification is needed to avoid misdiagnosis of COVID-19 virus as a common viral infection because COVID-19 virus infection has a different line of treatment. In addition, Abhiyev [ 24 ] et al, Tariq [ 25 ] et al, Bharati et al [ 26 ] and Apostolopoulos et al [ 27 ] proposed pulmonary chest disease classification by applying deep learning approaches. Research is beginning to focus on show the identification of COVID-19 cases with a variety of other pulmonary diseases like Fibrosis, Edema, and Effusion etc.…”

Section: State-of-the-art Methodsmentioning

confidence: 99%

A deep learning approach to detect Covid-19 coronavirus with X-Ray images

Jain

Mittal

Thakur

et al. 2020

Biocybernetics and Biomedical Engineering

261

170

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Rapid and accurate detection of COVID-19 coronavirus is necessity of time to prevent and control of this pandemic by timely quarantine and medical treatment in absence of any vaccine. Daily increase in cases of COVID-19 patients worldwide and limited number of available detection kits pose difficulty in identifying the presence of disease. Therefore, at this point of time, necessity arises to look for other alternatives. Among already existing, widely available and low-cost resources, X-ray is frequently used imaging modality and on the other hand, deep learning techniques have achieved state-of-the-art performances in computer-aided medical diagnosis. Therefore, an alternative diagnostic tool to detect COVID-19 cases utilizing available resources and advanced deep learning techniques is proposed in this work. The proposed method is implemented in four phases, viz., data augmentation, preprocessing, stage-I and stage-II deep network model designing. This study is performed with online available resources of 1215 images and further strengthen by utilizing data augmentation techniques to provide better generalization of the model and to prevent the model overfitting by increasing the overall length of dataset to 1832 images. Deep network implementation in two stages is designed to differentiate COVID-19 induced pneumonia from healthy cases, bacterial and other virus induced pneumonia on X-ray images of chest. Comprehensive evaluations have been performed to demonstrate the effectiveness of the proposed method with both (i) training-validation-testing and (ii) 5-fold cross validation procedures. High classification accuracy as 97.77%, recall as 97.14% and precision as 97.14% in case of COVID-19 detection shows the efficacy of proposed method in present need of time. Further, the deep network architecture showing averaged accuracy/sensitivity/specificity/precision/F1-score of 98.93/98.93/98.66/96.39/98.15 with 5-fold cross validation makes a promising outcome in COVID-19 detection using X-ray images.

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Section: State-of-the-art Methodsmentioning

confidence: 99%

A deep learning approach to detect Covid-19 coronavirus with X-Ray images

Jain

Mittal

Thakur

et al. 2020

Biocybernetics and Biomedical Engineering

261

170

View full text Add to dashboard Cite

show abstract

“…Hence, it is justified to analyze these parameters while investigating the lung cancer probabilities. Instead of being focused to CT image related classification procedure as carried out by [11][12][13][14][15][16][17][18], past or current habits of patients can be utilized in the domain of lung disease classification tool. It is the leading cause of years of life lost because it is associated with the highest economic burden relative to other tumour types.…”

Section: Discussionmentioning

confidence: 99%

“…Using two multiple resolution residually connected network (MRRN) formulations called incremental-MRRN and dense-MRRN, detect and segment the lung tumors CT images [16]. A new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN is proposed in [17]. As shown in [18], lung cancer prognosis can be carried out by implementing and comparing data mining classifier models such as, Naïve Bayes, K-Nearest Neighbors (KNN), Logistic Regression, Tree, Random Forest, and Neural Network.…”

Section: Related Workmentioning

confidence: 99%

Early Lung Cancer Prediction Using Neural Network with Cross-validation

Dutta¹,

Bandyopadhyay²

2020

AJRID

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Lung cancer is known as lung carcinoma. It is a disease which is malignant tumor leading to the uncontrolled cell growth in the lung tissue. Lung cancer is caused generally by smoking and the use of tobacco products. It is classified into two broad Small-cell lung Carcinomas and non-Small cell lung carcinomas. Lung cancer treatments include surgery, radiation therapy, chemotherapy, and targeted therapy. Lung Cancer disease is one of the most prominent cause of death in all over world. Early detection of this disease can assist medical care unit as well as physicians to provide counter measures to the patients. The objective of this paper is to approach an automated tool that takes influential causes of lung cancer as input and detect patients with higher probabilities of being affected by this disease. A neural network classifier accompanied by k-fold cross-validation technique is proposed in this paper as a predictive tool. Later, this proposed method is compared with another baseline classifier Gradient Boosting Classifier in order to justify the prediction performance. Experimental results conclude that analyzing interfering causes of lung cancer can effectively accomplish disease classification model with an accuracy of 95%.

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“…Deep learning architectures included visual geometry group (VGG)-16 or VGG-19, Resnet 50, Xception, DenseNet201, Inception_ResNet_V2 and Inception_V3 with data sources that ranged from Kaggle, GitHub, and various hospitals, especially from cities in China. Impressive variations of CNN for medical imaging included: combined CNN-LSTM network ( 6 ), faster regions with CNN ( 7 ), and a hybrid VGG-based neural network and data augmentation and spatial transformer network (STN) with CNN (VDSNet)( 8 ). There were also reports of using synthetic data from generative adversarial networks (GANs)( 9 ).…”

Section: Global Health Primer With Relevance To Artificial Intelligenmentioning

confidence: 99%

Artificial intelligence and COVID-19: Present state and future vision

Chang¹

2020

Intelligence-Based Medicine

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The COVID-19 pandemic has lead to catastrophic number of deaths and revealed that much work still remains with data and artificial intelligence. To fully comprehend the dynamics of a pandemic with relevance to artificial intelligence, a primer on global health concepts is first presented. Following this, various aspects of diagnosis and therapy and the relationship to artificial intelligence are presented along with a future projection of an ideal deployment of artificial intelligence in a pandemic. Final thoughts are made about lessons learned and what lies ahead.

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Hybrid deep learning for detecting lung diseases from X-ray images

Cited by 293 publications

References 27 publications

A deep learning approach to detect Covid-19 coronavirus with X-Ray images

A deep learning approach to detect Covid-19 coronavirus with X-Ray images

Early Lung Cancer Prediction Using Neural Network with Cross-validation

Artificial intelligence and COVID-19: Present state and future vision

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