A bagging dynamic deep learning network for diagnosing COVID-19

Zhang, Zhijun; Chen, Bozhao; Sun, Jiansheng; Luo, Yu

doi:10.1038/s41598-021-95537-y

Cited by 15 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhang et al 21 developed the bagging dynamic deep learning network (BDLLN) for the detection of covid-19 based on the symptoms in chest X-ray images. They constructed the B-DDLN network using five convolution layers followed by pooling layers for automated feature extraction from the chest X-ray images.…”

Section: Discussionmentioning

confidence: 99%

Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques

Guhan

Almutairi

Sowmiya

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their performance with machine learning classifiers. In machine learning, the Naive Bayes classifier achieved the highest accuracy of 97%, whereas the ResNet50 CNN model attained the highest accuracy of 99%. Hence the deep learning networks outperformed well compared to the machine learning techniques in the classification of Covid-19 images.

show abstract

Section: Discussionmentioning

confidence: 99%

Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques

Guhan

Almutairi

Sowmiya

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…• Merge predictions from several models (Jahrer et al, 2010); • Merge the predictions obtained on different data representations (Kotsiantis et al, 2007); • Bagging (Mi et al, 2019) (Zhang et al, 2021) (Hothorn & Lausen, 2003); • Stacking (Deng et al, 2012).…”

Section: Improving Performance Using Combination Methodsmentioning

confidence: 99%

A New Hybrid Model of Deep Learning ResNeXt-SVM for Weed Detection

Jabir

Falih

2022

International Journal of Intelligent Information Technologies

View full text Add to dashboard Cite

A set of experiments has shown that deep learning as well as traditional learning can be used in the weed detection process and perform well, although sometimes these models cannot fully exploit and utilize the long-term dependency relationship between some key features of images and image labels. To remedy this known problem in the field of image classification we have introduced a classifier known as the Linear Support Vector Machine (SVM). Specifically, we have combined a ResNeXt and SVM network to provide the ResNeXt-SVM framework that can deepen the exploitation of the structured features of images and the understanding of their content. The experimental results show that compared to other algorithm models such as ResNet, ResNeXt and VGG, our proposed solution is more precise and efficient in classifying weeds.

show abstract

“…Afshar et al 27 proposed COVID-CAPS, an alternative modeling framework based on Capsule Networks, capable of handling small datasets and achieving high accuracy and specificity. Zhang et al 28 propose B-DDLN, which consists of a feature extractor made of convolution blocks followed by a bagging classifier made of N dynamic learning networks. Stubblefield et al 29 investigate the utility of Deep Neural Networks as feature extractors for classical networks such as XGBoost to be applied to smaller datasets.…”

Section: Related Workmentioning

confidence: 99%

Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19

Antony,

Kakileti,

Shah

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Healthcare delivery during the initial days of outbreak of COVID-19 pandemic was badly impacted due to large number of severely infected patients posing an unprecedented global challenge. Although the importance of Chest X-rays (CXRs) in meeting this challenge has now been widely recognized, speedy diagnosis of CXRs remains an outstanding challenge because of fewer Radiologists. The exponential increase in Smart Phone ownership globally, including LMICs, provides an opportunity for exploring AI-driven diagnostic tools when provided with large volumes of CXRs transmitted through Smart Phones. However, the challenges associated with such systems have not been studied to the best of our knowledge. In this paper, we show that the predictions of AI-driven models on CXR images transmitted through Smart Phones via applications, such as , suffer both in terms of Predictability and Explainability, two key aspects of any automated Medical Diagnosis system. We find that several existing Deep learning based models exhibit prediction instability–disagreement between the prediction outcome of the original image and the transmitted image. Concomitantly we find that the explainability of the models deteriorate substantially, prediction on the transmitted CXR is often driven by features present outside the lung region, clearly a manifestation of Spurious Correlations. Our study reveals that there is significant compression of high-resolution CXR images, sometimes as high as 95%, and this could be the reason behind these two problems. Apart from demonstrating these problems, our main contribution is to show that Multi-Task learning (MTL) can serve as an effective bulwark against the aforementioned problems. We show that MTL models exhibit substantially more robustness, 40% over existing baselines. Explainability of such models, when measured by a saliency score dependent on out-of-lung features, also show a 35% improvement. The study is conducted on WaCXR dataset, a curated dataset of 6562 image pairs corresponding to original uncompressed and compressed CXR images. Keeping in mind that there are no previous datasets to study such problems, we open-source this data along with all implementations.

show abstract

A bagging dynamic deep learning network for diagnosing COVID-19

Cited by 15 publications

References 43 publications

Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques

Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques

A New Hybrid Model of Deep Learning ResNeXt-SVM for Weed Detection

Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19

Contact Info

Product

Resources

About