Multiclass COVID-19 detection by utilizing ESN-MDFS: Extreme Smart Network using mean dropout feature selection technique.

Multiclass COVID-19 detection by utilizing ESN-MDFS: Extreme Smart Network using mean dropout feature selection technique.

<p>Multiclass COVID-19 detection by utilizing ESN-MDFS: Extreme Smart Network using mean dropout feature selection technique.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Saghir Ahmed (4416544) (author)
مؤلفون آخرون:	Basit Raza (20175794) (author), Lal Hussain (14100502) (author), Touseef Sadiq (20175797) (author), Ashit Kumar Dutta (11547381) (author)
منشور في:	2024
الموضوعات:	Cancer Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified publicly available sources promising significant advancements portable chest x novel approach called large dataset containing extreme smart network 000 cxr images model &# 8217 images encompass covid enhancing multiclass covid enhancing covid prediction mdfs model achieved 16 model 19 prediction trained vgg superior ability study explores study aimed research proposes optimize performance normal conditions normal cases meticulously addressed mdfs ). hyperparameter tuning fold cross findings demonstrate enhance multi data imbalance cxrs ). bacterial pneumonia
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مواد مشابهة

Confusion matrix to distinguish multiclass COVID-19 detection by utilizing ESN-MDFS: Extreme Smart Network using mean dropout feature selection technique.
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Multiclass COVID-19 detection by utilizing ESN: Extreme Smart Network without MeanDropout feature space.
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Comparison of Multiclass COVID-19 detection by utilizing ESN-MDFS with existing Lightweight models.
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Confusion Matrix to distinguish Multiclass COVID-19 detection by utilizing ESN: Extreme Smart Network without MeanDropout feature space.
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Area under the receiver operating characteristic curve (AUC) to distinguish Multiclass COVID-19 detection by utilizing ESN-MDFS.
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Fold vs accuracy curve to distinguish multi-class using ESN-MDFS.
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Data Augmentation techniques on multiclass.
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Comparison of AI-assisted recent studies for COVID-19 lung infection.
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Hyperparameters optimization of XGBoost algorithm.
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Fig 6 -
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Fig 5 -
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

General architecture of XGBoost algorithm.
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Image pre-processing steps.
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

Fig 1 -
حسب: Saghir Ahmed (4416544)
منشور في: (2024)

(A) Agreement of CAD software with radiologists, at manufacturer suggested thresholds for normal vs. abnormal CXRs.
حسب: Sam Linsen (20606266)
منشور في: (2025)

Software performance at a set radiologist observed sensitivity and specificity.
حسب: Sam Linsen (20606266)
منشور في: (2025)

Radiologist accuracy in identifying COVID-19 on CXR in comparison to laboratory test results.
حسب: Sam Linsen (20606266)
منشور في: (2025)

Radiologist interpretation of CXRs in comparison to laboratory testing results.
حسب: Sam Linsen (20606266)
منشور في: (2025)

Demographic and clinical characteristics from included participants PCR: Sars CoV-2 polymerase chain reaction testing, Antigen: Sars CoV-2 Antigen test.
حسب: Sam Linsen (20606266)
منشور في: (2025)

Fig 5 -
حسب: Sam Linsen (20606266)
منشور في: (2025)

Software and radiologist performance at diagnosing COVID-19 before and after the shift to Omicron being the dominant variant.
حسب: Sam Linsen (20606266)
منشور في: (2025)

Performance of radiologist and software CAD1 and CAD2 at identifying Covid-19 in comparison to baseline molecular testing.
حسب: Sam Linsen (20606266)
منشور في: (2025)

Aggregate performance and 95% confidence intervals of radiologist and software CAD1 and CAD2 at identifying Covid-19 in comparison to baseline molecular testing. AUC: Area under Curve, RadRS: Radiologist reference standard for COVID-19.
حسب: Sam Linsen (20606266)
منشور في: (2025)

Fig 1 -
حسب: Sam Linsen (20606266)
منشور في: (2025)

Subgroup analysis of performance of radiologist and software CAD1 and CAD2 at identifying COVID-19 in comparison to baseline molecular testing grouped by HIV status.
حسب: Sam Linsen (20606266)
منشور في: (2025)

Subgroup analysis of performance of radiologist and software CAD1 and CAD2 at identifying COVID-19 in comparison to baseline molecular testing grouped by diabetes status.
حسب: Sam Linsen (20606266)
منشور في: (2025)

S2 Fig -
حسب: Sam Linsen (20606266)
منشور في: (2025)

Subgroup analysis of performance of radiologist and software CAD1 and CAD2 at identifying COVID-19 in comparison to baseline molecular testing grouped by age.
حسب: Sam Linsen (20606266)
منشور في: (2025)

Comparison of severity scoring performance across prior studies and the proposed severity scoring module. MAE: Mean Absolute Error.
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

Summary of reported AUC values from previous studies and the proposed model across different CXR-based pneumonia detection tasks. All studies had close collaboration with radiologists for data interpretation and validation.
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

Evaluation of the severity scoring model’s robustness to inter-observer variability. The model was trained using the average scores from four radiologists. During testing, one radiologist was excluded at a time, and performance was compared against the remaining consensus. Results include overall Pearson correlation and MAE, along with subgroup MAEs across three severity levels: Group 1 (SI ≤ 8), Group 2 (9 ≤ SI < 16), and Group 3 (SI ≥ 16). Pearson Co.: Pearson correlation. MAE: mean absolute error. SI: Severity score Index.
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

External validation results of the diagnostic model on the COVIDGR-1.0 dataset, compared with previously published models (COVIDNet-CXR and COVID-CAPS). Performance metrics include class-wise recall, precision, F1-score, and overall accuracy.
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

Ablation study in the second diagnosis scenario (including all 808 COVID-19 CXRs) over the five-fold cross-validation approach. In all experiments, the segmented thorax regions have been used as the model input.
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

Ablation study in the firs diagnosis scenario (excluding COVID-19 CXRs with no evidence of infection) over the five-fold cross-validation approach.
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

Design of experiments. TL: Transfer Learning.
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

Performance comparison across architectures for differentiating COVID-19 from other viral pneumonia and normal cases. The presented results are the Mean ± SD of the obtained results through a five-fold cross-validation process. The best results have been highlighted in bold. The results have been reported for the first diagnosis scenario.
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

COVID-19 sensitivity (%) of the proposed DL diagnostic model compared to four individual radiologists and their consensus, stratified by severity level. Severity was categorized based on radiographic severity scores: low (≤ 8), medium (8–16), and severe (> 16). Sensitivity is reported as mean ± standard deviation across eight randomized test splits.
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

The performance of individual radiologists, their consensus, and the DL model in the second diagnostic scenario (including all 808 COVID-19 cases).
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

F1-scores and accuracy (95% CI) for individual radiologists, consensus, and the DL model.
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

The performance of individual radiologists, their consensus, and the DL model in the first diagnostic scenario (excluding COVID-19 CXRs with a severity score of zero as assessed by at least three radiologists).
حسب: Nastaran Enshaei (9405653)
منشور في: (2025)

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