Pneumonia Detection
18 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Pneumonia Detection
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ChestX-ray14
Latest papers with no code
Improving Pediatric Pneumonia Diagnosis with Adult Chest X-ray Images Utilizing Contrastive Learning and Embedding Similarity
Despite the advancement of deep learning-based computer-aided diagnosis (CAD) methods for pneumonia from adult chest x-ray (CXR) images, the performance of CAD methods applied to pediatric images remains suboptimal, mainly due to the lack of large-scale annotated pediatric imaging datasets.
A novel method to enhance pneumonia detection via a model-level ensembling of CNN and vision transformer
Our fusion architecture combines a ResNet34 variant and a Multi-Axis Vision Transformer small model.
Pneumonia Detection on chest X-ray images Using Ensemble of Deep Convolutional Neural Networks
Pneumonia is a life-threatening lung infection resulting from several different viral infections.
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection
Additionally, we show that the prototypical in-distribution samples identified by our proposed methods translate well to different models and other datasets and that using their characteristics as guidance allows for successful manual selection of small subsets of high-performing samples.
Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning
Using more than 5800 chest X-ray images for pneumonia detection from a publicly available dataset, we ran experiments to assess the effectiveness of our approach.
Prediction of Pneumonia and COVID-19 Using Deep Neural Networks
We evaluate the performance of different machine learning models, including DenseNet121, Inception Resnet-v2, Inception Resnet-v3, Resnet50, and Xception, using chest X-ray images of pneumonia patients.
Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance
One of the major hurdles faced by the Artificial Intelligence (AI) research community is the lack of publicly available datasets for chest diseases, including pneumonia .
SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model
However, the traditional diagnostic tool design methods based on supervised learning are burdened by the need to provide training data annotation, which should be of good quality for better clinical outcomes.
From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning
Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data science competition standpoint, have limited utility in clinical use because of their narrow focus on diagnosing one specific disease.
Calibrating for Class Weights by Modeling Machine Learning
A much studied issue is the extent to which the confidence scores provided by machine learning algorithms are calibrated to ground truth probabilities.
