{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-covid-predicting-covid-19-from-chest-x","title":"Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning","arxiv_id":"2004.09363","date":"2020-04-20","proceeding":null,"authors":["Shervin Minaee","Rahele Kafieh","Milan Sonka","Shakib Yazdani","Ghazaleh Jamalipour Soufi"],"abstract":"The novel corona-virus (also known as Covid-19) has led to a pandemic, impacting more than 200 countries across the globe. With this huge scale, there are a very large number of tweets coming out from every corner of the world, about Covid-19. Analyzing the tweets and detecting the major topics and concerns people are posting about, can help us to better understand the situation, and come up with better planning. In this work, we propose a model based on sentence Transformer to detect the main topics of Tweets in recent months. The proposed model first learns sentence-level representation of tweets, and then group them based on their embedding similarities into some groups, and then detect the most important words in each cluster based on their frequency and average similarity to other words. Through experimental results, we show that our model can detect very informative topics, by processing the tweets on sentence level (which can preserve the overall meaning of the tweets). The proposed model is trained in an unsupervised fashion, and can be applied to any dataset our textual data from social media websites.","url_abs":"https://arxiv.org/abs/2004.09363v3","url_pdf":"https://arxiv.org/pdf/2004.09363v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-covid-predicting-covid-19-from-chest-x","repo_url":"https://github.com/shervinmin/DeepCovid","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-block","method_name":"Dense Block"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"fire-module","method_name":"Fire Module"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"squeezenet","method_name":"SqueezeNet"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"xavier-initialization","method_name":"Xavier Initialization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}