Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification

By Daniel S. Kermany, Kang Zhang, Michael H. Goldbaum. 2018

799 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging, Surgery

View Paper: https://www.semanticscholar.org/paper/e4dc92173448860e848febe9f5629c990e36af88

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Youness Chawki
2. Khalid El Asnaoui

In Radiology, Nuclear Medicine and Imaging

1. Youness Chawki
2. Khalid El Asnaoui
3. Mangal Sain
4. Do-Un Jeong
5. Okeke Stephen
6. Uchenna Joseph Maduh

In Surgery

1. Youness Chawki
2. Khalid El Asnaoui

Citations in the corpus

• Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19 (2021, neutral): “In terms of classification between non-COVID-19 viral pneumonia and healthy CXR images, several studies utilized same datasets made available by Kermany et al. [54].”
• An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare (2019, methodology): “Our experiments were based on a chest X-ray image dataset proposed in [29].”
• Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning (2019, neutral): “[23].”
• Using X-ray images and deep learning for automated detection of coronavirus disease (2020, methodology): “The first dataset (Kermany et al., 2018) is named chest X-ray & CT dataset and composed of 5856 images and has two categories (4273 pneumonia and 1583 normal) whereas the second one is named Covid Chest X-ray Dataset (Cohen et al., 2020).”
• A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images (2020, neutral): “For this purpose, we have used 206 CXR images of COVID9 infected patients from the COVID-chest X-ray-dataset [18] and 64 CXR images of Normal patients from the CXR Images (Pneu- onia) dataset [20,21] , the count of the cases are highlighted in ig.”
• CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization (2020, neutral): “One of the datasets used in this study is a collection of total 5856 images consisting 1583 normal X-rays, 1493 non-COVID viral pneumonia X-rays and 2780 bacterial pneumonia X-rays collected in Guangzhou Medical Center, China [20].”
• COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images (2020, neutral): “In order to compose a special COVID-19 dataset, two different publicly available datasets were combined as COVID chest X-ray dataset [12] and Kaggle chest X-ray pneumonia dataset [38].”