REVIEW 2 cited by
Towards Radiologist-Level Accurate Deep Learning System for Pulmonary Screening
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In this work, we propose advanced pneumonia and Tuberculosis grading system for X-ray images. The proposed system is a very deep fully convolutional classification network with online augmentation that outputs confidence values for diseases prevalence. Its a fully automated system capable of disease feature understanding without any offline preprocessing step or manual feature extraction. We have achieved state- of-the- art performance on the public databases such as ChestXray-14, Mendeley, Shenzhen Hospital X-ray and Belarus X-ray set.
Forward citations
Cited by 2 Pith papers
-
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm
An Xception-plus-NGBoost pipeline tuned by African Vultures Optimization reaches 97.53% accuracy and 97.72% F1 on the Monkeypox Skin Lesion Dataset, but the evaluation uses augmented images and may leak patient data a...
-
Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images
A weighted average of MobileNetV2 and NASNetMobile is reported to reach 98.63% accuracy on the Kermany pediatric pneumonia dataset, though the ensemble weights were optimized on the test set.
Discussion (0). Continue with ORCID to comment.