Development and validation of a hybrid deep learning–machine learning approach for severity assessment of COVID-19 and other pneumonias

Park, Doohyun; Jang, Ryoungwoo; Chung, Myung Jin; An, Hyun Joon; Bak, Seongwon; Choi, Euijoon; Hwang, Dosik

doi:10.1038/s41598-023-40506-w

Cited by 7 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applying the experimental results to the 30% testing data (i.e., 2766 images out of 9220) demonstrates the superiority of the hybrid DTL approach when coupled with NN, achieving the highest MCC of 0.814, followed by SVM with a linear kernel, which yielded an MCC of 0.805 when compared to existing pre-trained models of densely connected classifiers in the binary classification task. Others have proposed deep learning approaches to detect COVID-19 using X-ray and CT images [9][10][11][12][13][14]. Table 1 provides an overview of the existing works compared to our proposed work.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach

Turki,

Al Habib,

Taguchi

2024

Mathematics

View full text Add to dashboard Cite

Transmission electron microscopy imaging provides a unique opportunity to inspect the detailed structure of infected lung cells with SARS-CoV-2. Unlike previous studies, this novel study aims to investigate COVID-19 classification at the lung cellular level in response to SARS-CoV-2. Particularly, differentiating between healthy and infected human alveolar type II (hAT2) cells with SARS-CoV-2. Hence, we explore the feasibility of deep transfer learning (DTL) and introduce a highly accurate approach that works as follows: First, we downloaded and processed 286 images pertaining to healthy and infected hAT2 cells obtained from the electron microscopy public image archive. Second, we provided processed images to two DTL computations to induce ten DTL models. The first DTL computation employs five pre-trained models (including DenseNet201 and ResNet152V2) trained on more than one million images from the ImageNet database to extract features from hAT2 images. Then, it flattens and provides the output feature vectors to a trained, densely connected classifier with the Adam optimizer. The second DTL computation works in a similar manner, with a minor difference in that we freeze the first layers for feature extraction in pre-trained models while unfreezing and jointly training the next layers. The results using five-fold cross-validation demonstrated that TFeDenseNet201 is 12.37× faster and superior, yielding the highest average ACC of 0.993 (F1 of 0.992 and MCC of 0.986) with statistical significance ( from a t-test) compared to an average ACC of 0.937 (F1 of 0.938 and MCC of 0.877) for the counterpart (TFtDenseNet201), showing no significance results ( from a t-test).

show abstract

Section: Introduction and Related Workmentioning

confidence: 99%

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach

Turki,

Al Habib,

Taguchi

2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…This method divided the lung regions into several subdivisions and assigned scores ranging from 0-3. Another author in 35 created and validated a severity assessment model encompassing COVID-19, influenza, and novel influenza, utilizing CT images from a diverse multi-center dataset and reported that the developed model also applies to patients with other types of viral pneumonia. In the proposed architecture, we have employed the modified the RALE scoring system introduced 12 , 32 to assess COVID-19 CXR image severity.…”

Section: Introductionmentioning

confidence: 99%

A multistage framework for respiratory disease detection and assessing severity in chest X-ray images

Sahoo,

Sharma,

Saha

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Chest Radiography is a non-invasive imaging modality for diagnosing and managing chronic lung disorders, encompassing conditions such as pneumonia, tuberculosis, and COVID-19. While it is crucial for disease localization and severity assessment, existing computer-aided diagnosis (CAD) systems primarily focus on classification tasks, often overlooking these aspects. Additionally, prevalent approaches rely on class activation or saliency maps, providing only a rough localization. This research endeavors to address these limitations by proposing a comprehensive multi-stage framework. Initially, the framework identifies relevant lung areas by filtering out extraneous regions. Subsequently, an advanced fuzzy-based ensemble approach is employed to categorize images into specific classes. In the final stage, the framework identifies infected areas and quantifies the extent of infection in COVID-19 cases, assigning severity scores ranging from 0 to 3 based on the infection’s severity. Specifically, COVID-19 images are classified into distinct severity levels, such as mild, moderate, severe, and critical, determined by the modified RALE scoring system. The study utilizes publicly available datasets, surpassing previous state-of-the-art works. Incorporating lung segmentation into the proposed ensemble-based classification approach enhances the overall classification process. This solution can be a valuable alternative for clinicians and radiologists, serving as a secondary reader for chest X-rays, reducing reporting turnaround times, aiding clinical decision-making, and alleviating the workload on hospital staff.

show abstract

M3F: Multi-Field-of-View Feature Fusion Network for Aortic Vessel Tree Segmentation in CT Angiography

Byeon,

Kim,

Kim

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Development and validation of a hybrid deep learning–machine learning approach for severity assessment of COVID-19 and other pneumonias

Cited by 7 publications

References 38 publications

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach

A multistage framework for respiratory disease detection and assessing severity in chest X-ray images

M3F: Multi-Field-of-View Feature Fusion Network for Aortic Vessel Tree Segmentation in CT Angiography

Contact Info

Product

Resources

About