ECOVNet: a highly effective ensemble based deep learning model for detecting COVID-19

Chowdhury, Nihad Karim; Kabir, Muhammad Ashad; Rahman, Md. Muhtadir; Hossain, Mohammad Shahadat

doi:10.7717/peerj-cs.551

Cited by 38 publications

(24 citation statements)

References 28 publications

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“…In contrast to this, these models are more suited for recognizing several disease kinds [ 3 ] or for more complex problems, such as segmentation. Additionally, most of the researchers [ 3 , 9 , 13 , 20 , 23 , 24 , 34 , 47 , 65 , 86 , 89 , 102 ] believe that when the number of CNN layers rises for a given binary classification task, these classifiers did not work appropriately.…”

Section: Resultsmentioning

confidence: 99%

“…This model has achieved a recall of 95.74%, a specificity of 96.77%, an accuracy of 94.29%, and a precision of 93.75%. When using chest x-rays to detect COVID-19, pneumonia, and normal patients, Chowdhury et al [ 13 ] developed the CNN-based EfficientNet model known as ECOVNet. The ECOVNet model has a detection accuracy of chest illnesses of 96.07%.…”

Section: Resultsmentioning

confidence: 99%

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CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays

Malik

Anees

din

et al. 2022

Multimed Tools Appl

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Coronavirus (COVID-19) has adversely harmed the healthcare system and economy throughout the world. COVID-19 has similar symptoms as other chest disorders such as lung cancer (LC), pneumothorax, tuberculosis (TB), and pneumonia, which might mislead the clinical professionals in detecting a new variant of flu called coronavirus. This motivates us to design a model to classify multi-chest infections. A chest x-ray is the most ubiquitous disease diagnosis process in medical practice. As a result, chest x-ray examinations are the primary diagnostic tool for all of these chest infections. For the sake of saving human lives, paramedics and researchers are working tirelessly to establish a precise and reliable method for diagnosing the disease COVID-19 at an early stage. However, COVID-19’s medical diagnosis is exceedingly idiosyncratic and varied. A multi-classification method based on the deep learning (DL) model is developed and tested in this work to automatically classify the COVID-19, LC, pneumothorax, TB, and pneumonia from chest x-ray images. COVID-19 and other chest tract disorders are diagnosed using a convolutional neural network (CNN) model called CDC Net that incorporates residual network thoughts and dilated convolution. For this study, we used this model in conjunction with publically available benchmark data to identify these diseases. For the first time, a single deep learning model has been used to diagnose five different chest ailments. In terms of classification accuracy, recall, precision, and f1-score, we compared the proposed model to three CNN-based pre-trained models, such as Vgg-19, ResNet-50, and inception v3. An AUC of 0.9953 was attained by the CDC Net when it came to identifying various chest diseases (with an accuracy of 99.39%, a recall of 98.13%, and a precision of 99.42%). Moreover, CNN-based pre-trained models Vgg-19, ResNet-50, and inception v3 achieved accuracy in classifying multi-chest diseases are 95.61%, 96.15%, and 95.16%, respectively. Using chest x-rays, the proposed model was found to be highly accurate in diagnosing chest diseases. Based on our testing data set, the proposed model shows significant performance as compared to its competitor methods. Statistical analyses of the datasets using McNemar’s, and ANOVA tests also showed the robustness of the proposed model.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays

Malik

Anees

din

et al. 2022

Multimed Tools Appl

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“…Their dataset included only 219 COVID-19 positive, but 1341 normal and 1345 viral pneumonia images. ECOVNet [ 75 ], known as an ensemble of CNN based on EfficientNet, was later proposed to detect COVID-19 from X-ray images. A total of 589 COVID-19 in a total of 13,914 chest X-ray images was used.…”

Section: Resultsmentioning

confidence: 99%

Feature-level ensemble approach for COVID-19 detection using chest X-ray images

Gwak

2022

PLoS ONE

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Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as the coronavirus disease 2019 (COVID-19), has threatened many human beings around the world and capsized economies at unprecedented magnitudes. Therefore, the detection of this disease using chest X-ray modalities has played a pivotal role in producing fast and accurate medical diagnoses, especially in countries that are unable to afford laboratory testing kits. However, identifying and distinguishing COVID-19 from virtually similar thoracic abnormalities utilizing medical images is challenging because it is time-consuming, demanding, and susceptible to human-based errors. Therefore, artificial-intelligence-driven automated diagnoses, which excludes direct human intervention, may potentially be used to achieve consistently accurate performances. In this study, we aimed to (i) obtain a customized dataset composed of a relatively small number of images collected from publicly available datasets; (ii) present the efficient integration of the shallow handcrafted features obtained from local descriptors, radiomics features specialized for medical images, and deep features aggregated from pre-trained deep learning architectures; and (iii) distinguish COVID-19 patients from healthy controls and pneumonia patients using a collection of conventional machine learning classifiers. By conducting extensive experiments, we demonstrated that the feature-based ensemble approach provided the best classification metrics, and this approach explicitly outperformed schemes that used only either local, radiomic, or deep features. In addition, our proposed method achieved state-of-the-art multi-class classification results compared to the baseline reference for the currently available COVID-19 datasets.

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“…Our model was based on the recently proposed EfficientNet that achieved better accuracy and efficiency (6.1 times faster) with a smaller number of model parameters (8.4 times less) than other networks [23]. It has been applied to solve medical problems, such as the diagnosis of COVID-19 [24,25] and the classification of skin diseases [26]. EfficientNet utilizes a new scaling method, so-called compound coefficient, to systematically balance the depth (d: the length of the model), width (w: the number of channels) and resolution (r: the image size) of a given network.…”

Section: Effeicient-b0 Networkmentioning

confidence: 99%

Ensembled Deep Neural Network for Intracranial Hemorrhage Detection and Subtype Classification on Noncontrast CT Images

Wu¹,

Supanich²,

Deng³

2021

JoAIMS

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Rapid and accurate diagnosis of intracranial hemorrhage is clinically significant to ensure timely treatment. In this study, we developed an ensembled deep neural network for the detection and subtype classification of intracranial hemorrhage. The model consisted of two parallel network pathways, one using three different window level/width settings to enhance the image contrast of brain, blood, and soft tissue. The other extracted spatial information of adjacent image slices to the target slice. Both pathways exploited the EfficientNet-B0 as the basic architecture and were ensembled to generate the final prediction. Class activation mapping was applied in both pathways to highlight the regions of detected hemorrhage and the associated subtypes. The model was trained and tested using Intracranial Hemorrhage Detection Challenge (IHDC) dataset launched by the Radiological Society of North America (RSNA) in 2019, which contained 674,258 head noncontrasts computer tomography images acquired from 19,530 patients. An independent dataset (CQ500) acquired from another institution was used to test the generalizability of the trained model. The overall accuracy, sensitivity, and F1 score for intracranial hemorrhage detection were 95.7%, 85.9%, and 86.7% on IHDC testing dataset and 92.4%, 92.6%, and 93.4% on external CQ500 testing dataset. The heatmaps by class activation mapping successfully demonstrated discriminative feature regions of the predicted hemorrhage locations and subtypes, providing visual guidance for radiologists to assist in rapid diagnosis of intracranial hemorrhage.

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ECOVNet: a highly effective ensemble based deep learning model for detecting COVID-19

Cited by 38 publications

References 28 publications

CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays

CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays

Feature-level ensemble approach for COVID-19 detection using chest X-ray images

Ensembled Deep Neural Network for Intracranial Hemorrhage Detection and Subtype Classification on Noncontrast CT Images

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