Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN

Nature·Nov 16

✦ NabkaNews BriefAuto-summarized from multiple outlets · verify with the source

Researchers are exploring the use of ensemble classification and deep learning frameworks to detect covid-19 and other diseases, such as lung cancer and liver disease, from medical imaging data. Various approaches are being investigated, including feature fusion, attention-based fusion, and federated learning. The specific applications and methods being studied appear to vary, with some focusing on covid-19 detection from ct scans and others on detecting different types of cancer from various imaging modalities.

Full coverage

An efficient dual path deep learning framework for COVID-19 classification using lung CT scans with explainable AI

Nature·Jan 24

Interpretable hybrid ensemble with attention-based fusion and EAOO-GA optimization for lung cancer detection

Nature·Mar 3

Privacy-preserving federated learning with optimized ensemble weighting and knowledge distillation for COVID-19 detection from non-IID medical imaging data

Nature·Apr 18

Detection and classification of lung cancer using sequential hybridization of CNN and RNN type architectures

Frontiers·Apr 13

Transformer-enhanced deep ensemble for multi-class liver disease classification using computed tomography images

Nature·Mar 9

Multimodal deep learning ensemble framework for skin cancer detection

Nature·Dec 30

HyFusion-X: hybrid deep and traditional feature fusion with ensemble classifiers for breast cancer detection using mammogram and ultrasound images

Nature·Nov 3

Enhancing tumor deepfake detection in MRI scans using adversarial feature fusion ensembles

Nature·Dec 9

View original report at Nature ↗

Fetching the latest headlines…