moloud1987/UncertaintyFuseNet-for-COVID-19-Classification
Code implementation of 'UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion with Ensemble Monte Carlo Dropout for COVID-19 Detection'
This project helps medical professionals quickly and accurately detect COVID-19 by analyzing chest CT scans and X-ray images. It takes these medical images as input and provides a classification of whether COVID-19 is present, along with an indication of the model's confidence in that prediction. Radiologists, intensivists, and other clinicians dealing with infectious diseases would find this valuable for computer-aided diagnosis.
No commits in the last 6 months.
Use this if you need a reliable automated system to classify COVID-19 cases from both CT scans and X-ray images, with an emphasis on understanding the prediction's certainty.
Not ideal if you are looking for a diagnostic tool that relies solely on clinical symptoms or lab test results rather than medical imaging.
Stars
22
Forks
4
Language
Jupyter Notebook
License
—
Last pushed
Jan 25, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
EmuKit/emukit
A Python-based toolbox of various methods in decision making, uncertainty quantification and...
google/uncertainty-baselines
High-quality implementations of standard and SOTA methods on a variety of tasks.
nielstron/quantulum3
Library for unit extraction - fork of quantulum for python3
IBM/UQ360
Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you...
aamini/evidential-deep-learning
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!