Pneumonia Detection in Chest Radiographs

2018/11/21 by The DeepRadiology Team
Medicine · #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Lung Cancer Diagnosis and Treatment #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.1811.08939

openalex publication_date 2018/11/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this work, we describe our approach to pneumonia classification and localization in chest radiographs. This method uses only open-source deep learning object detection and is based on CoupleNet, a fully convolutional network which incorporates global and local features for object detection. Our approach achieves robustness through critical modifications of the training process and a novel ensembling algorithm which merges bounding boxes from several models. We tested our detection algorithm tested on a dataset of 3000 chest radiographs as part of the 2018 RSNA Pneumonia Challenge; our solution was recognized as a winning entry in a contest which attracted more than 1400 participants worldwide.

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