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Comparison of the recommended VGGCOV19-NET COVID-19 diagnosis method with other <t> CNN </t> methods developed using radiology images
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Image Search Results


Comparison with previous researches

Journal: Journal of Imaging Informatics in Medicine

Article Title: Artificial Intelligence Application in Skull Bone Fracture with Segmentation Approach

doi: 10.1007/s10278-024-01156-0

Figure Lengend Snippet: Comparison with previous researches

Article Snippet: Skull R-CNN: a CNN-based network for the skull fracture detection , Zhuo Kuang , 2020 , CT , Skull , , , 45 , 25 , 10.

Techniques: Comparison, Biomarker Discovery, Computed Tomography

Comparison of the recommended VGGCOV19-NET COVID-19 diagnosis method with other  CNN  methods developed using radiology images

Journal: Neural Computing & Applications

Article Title: VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm

doi: 10.1007/s00521-022-06918-x

Figure Lengend Snippet: Comparison of the recommended VGGCOV19-NET COVID-19 diagnosis method with other CNN methods developed using radiology images

Article Snippet: Khan et al. [ ] classified three classes with a new Xception CNN architecture-based CNN model with an accuracy ratio of 95%, Ahammed et al. [ ] classified three classes with the deep CNN model they created with accuracy of 94%, Apostolopoulos and Mpesiana [ ] classified three classes with the pre-trained VGG19 architecture with accuracy of 93.48%, Aslan et al. [ ] classified three classes with the hybrid CNN architecture with accuracy of 98.70%, Hira et al. [ ] classified three classes with the specially ResNeXt-50 with accuracy of 97.55%, and Wang et al. [ ] classified three classes with the deep CNN model they called COVID-Net with an accuracy of 93.3%.

Techniques: Comparison, Biomarker Discovery, Modification, Imaging

Comparison between this work and previous studies.

Journal: Scientific Reports

Article Title: Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images

doi: 10.1038/s41598-025-94677-9

Figure Lengend Snippet: Comparison between this work and previous studies.

Article Snippet: , CNN-based model (DEMNET) , Kaggle, ADNI datasets , High performance in classifying AD stages , Accuracy: 95.23%, AUC: 97%, Kappa: 0.93 , Class imbalance handled via SMOTE , Lower performance on larger datasets , Robustness testing on ADNI.

Techniques: Comparison, Extraction, Imaging, Biomarker Discovery

Journal: Cell Reports Medicine

Article Title: Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH

doi: 10.1016/j.xcrm.2023.101016

Figure Lengend Snippet:

Article Snippet: CNN-based models for quantification of NASH histological features from H&E or Trichrome stained biopsies , PathAI , N/A.

Techniques: Software, Staining