M$^3$Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging

Qian, Xuelin; Fu, Huazhu; Shi, Wei; Chen, Tao; Fu, Yanwei; Shan, Fei; Xue, Xiangyang

doi:10.1109/jbhi.2020.3030853

Cited by 53 publications

(36 citation statements)

References 48 publications

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“…AD3D-MIL was trained and tested on 460 CT images. A multitask multislice deep learning system ( M 3 Lung-sys) was developed for screening of coronavirus-infected persons using CT images [ 6 ].…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

Iskanderani

Mehedi

Aljohani

et al. 2021

Journal of Healthcare Engineering

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The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.

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Section: Related Workmentioning

confidence: 99%

Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

Iskanderani

Mehedi

Aljohani

et al. 2021

Journal of Healthcare Engineering

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“…They fine-tuned four CNN models, including ResNet18, ResNet50, SqueezeNet, and DenseNet, and achieved promising results in several tasks. Various other studies have also been recently conducted on COVID-19 detection, employing several deep learning models with CT images [ 2 , [19] , [20] , [21] , [22] , [23] ] and Lung Ultrasound (LUS) [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19

Azouji

Sami

Taheri

et al. 2021

Computers in Biology and Medicine

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The world has experienced epidemics of coronavirus infections several times over the last two decades. Recent studies have shown that using medical imaging techniques can be useful in developing an automatic computer-aided diagnosis system to detect pandemic diseases with high accuracy at an early stage. In this study, a large margin piecewise linear classifier was developed to diagnose COVID-19 compared to a wide range of viral pneumonia, including SARS and MERS, using chest x-ray images. In the proposed method, a preprocessing pipeline was employed. Moreover, deep pre- and post-rectified linear unit (ReLU) features were extracted using the well-known VGG-Net19, which was fine-tuned to optimize transfer learning. Afterward, the canonical correlation analysis was performed for feature fusion, and fused deep features were passed into the LMPL classifier. The introduced method reached the highest performance in comparison with related state-of-the-art methods for two different schemes (normal, COVID-19, and typical viral pneumonia) and (COVID-19, SARS, and MERS pneumonia) with 99.39% and 98.86% classification accuracy, respectively.

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“…tion to learn 3D context features, it is memory-consuming and computationally expensive, which hinders it from being used in clinical practice. [13] adopts a feature refinement and aggregation strategy called RAhead to aggregate the slice features by attention mechanism, but it lacks high-level context features interaction modeling.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Deep Cross-Slice Features From CT Images For Multi-Class Pneumonia Classification

Cao

Jiang

Hou

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Computed Tomography (CT) scanning is widely used for chest diseases detection including pneumonia due to its diagnostic efficacy and efficiency. Recent studies have shown that the distribution of infection regions caused by COVID-19 in CT images is different from other pneumonia, and the COVID-19 cases are more likely to suffer severe and largearea infections. However, most deep learning methods only focus on intra-slice features and ignore cross-slice features. In this paper, we propose a novel two-stage method to fully exploit deep cross-slice features from volumetric CT data, including a dual-task supervised CNN and a context-aware Bi-LSTM. To further demonstrate the effectiveness of our model, we conduct extensive experiments on a chest CT imaging dataset with a total of 801 patients (250 healthy people, 238 191 H1N1 patients, and 122 CAP patients). The experimental results indicate the superiority of our proposed model on the multi-class pneumonia classification task.

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M$^3$Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging

Cited by 53 publications

References 48 publications

Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

A large margin piecewise linear classifier with fusion of deep features in the diagnosis of COVID-19

Exploiting Deep Cross-Slice Features From CT Images For Multi-Class Pneumonia Classification

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