COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images

Islam, Md. Mohaiminul; Hannan, Tanveer; Sarker, Laboni; Ahmed, Zakaria

doi:10.20944/preprints202005.0151.v3

Cited by 2 publications

(2 citation statements)

References 10 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, numerous studies in the literature have highlighted the efficacy of Densenet in analyzing CXR images compared to other commonly used models. 6,38,50,51 This justifies the rationale behind using the Dense-Net architecture 6 as the backbone of our own model.…”

Section: Ards Classification By State-of-the-art Modelssupporting

confidence: 54%

“…These factors contribute to a smaller number of required training parameters, aligning well with the constraints posed by our limited dataset. Furthermore, numerous studies in the literature have highlighted the efficacy of Densenet in analyzing CXR images compared to other commonly used models 6 , 38 , 50 , 51 . This justifies the rationale behind using the Dense-Net architecture 6 as the backbone of our own model.…”

Section: Resultsmentioning

confidence: 64%

See 1 more Smart Citation

Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays

Yahyatabar,

Jouvet,

Cheriet

2023

J. Med. Imag.

View full text Add to dashboard Cite

Acute respiratory distress syndrome (ARDS) is a life-threatening condition that can cause a dramatic drop in blood oxygen levels due to widespread lung inflammation. Chest radiography is widely used as a primary modality to detect ARDS due to its crucial role in diagnosing the syndrome, and the x-ray images can be obtained promptly. However, despite the extensive literature on chest x-ray (CXR) image analysis, there is limited research on ARDS diagnosis due to the scarcity of ARDS-labeled datasets. Additionally, many machine learning-based approaches result in high performance in pulmonary disease diagnosis, but their decisions are often not easily interpretable, which can hinder their clinical acceptance. This work aims to develop a method for detecting signs of ARDS in CXR images that can be clinically interpretable.Approach: To achieve this goal, an ARDS-labeled dataset of chest radiography images is gathered and annotated for training and evaluation of the proposed approach. The proposed deep classification-segmentation model, Dense-Ynet, provides an interpretable framework for automatically diagnosing ARDS in CXR images. The model takes advantage of lung segmentation in diagnosing ARDS. By definition, ARDS causes bilateral diffuse infiltrates throughout the lungs. To consider the local involvement of lung areas, each lung is divided into upper and lower halves, and our model classifies the resulting lung quadrants. Results:The quadrant-based classification strategy yields the area under the receiver operating characteristic curve of 95.1% (95% CI 93.5 to 96.1), which allows for providing a reference for the model's predictions. In terms of segmentation, the model accurately identifies lung regions in CXR images even when lung boundaries are unclear in abnormal images. Conclusions:This study provides an interpretable decision system for diagnosing ARDS, by following the definition used by clinicians for the diagnosis of ARDS from CXR images.

show abstract

Section: Ards Classification By State-of-the-art Modelssupporting

confidence: 54%

Section: Resultsmentioning

confidence: 64%

Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays

Yahyatabar,

Jouvet,

Cheriet

2023

J. Med. Imag.

View full text Add to dashboard Cite

show abstract

EDNC: Ensemble Deep Neural Network for COVID-19 Recognition

2022

View full text Add to dashboard Cite

The automatic recognition of COVID-19 diseases is critical in the present pandemic since it relieves healthcare staff of the burden of screening for infection with COVID-19. Previous studies have proven that deep learning algorithms can be utilized to aid in the diagnosis of patients with potential COVID-19 infection. However, the accuracy of current COVID-19 recognition models is relatively low. Motivated by this fact, we propose three deep learning architectures, F-EDNC, FC-EDNC, and O-EDNC, to quickly and accurately detect COVID-19 infections from chest computed tomography (CT) images. Sixteen deep learning neural networks have been modified and trained to recognize COVID-19 patients using transfer learning and 2458 CT chest images. The proposed EDNC has then been developed using three of sixteen modified pre-trained models to improve the performance of COVID-19 recognition. The results suggested that the F-EDNC method significantly enhanced the recognition of COVID-19 infections with 97.75% accuracy, followed by FC-EDNC and O-EDNC (97.55% and 96.12%, respectively), which is superior to most of the current COVID-19 recognition models. Furthermore, a localhost web application has been built that enables users to easily upload their chest CT scans and obtain their COVID-19 results automatically. This accurate, fast, and automatic COVID-19 recognition system will relieve the stress of medical professionals for screening COVID-19 infections.

show abstract

COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images

Cited by 2 publications

References 10 publications

Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays

Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays

EDNC: Ensemble Deep Neural Network for COVID-19 Recognition

Contact Info

Product

Resources

About