Pathological outcomes of covid-19 for lungs infections based on transfer learning technology

O. Alsaif, Mohammed L. Muammer
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Abstract

In 2019 a new Syndrome appear on the Large numbers of people like (High temperature, cough, Loss of sense of smell and taste(forcing a lot of them to enter the critical care unit after while the virus how case this syndrome named (SARS-CoV2). The aim of this paper is recognize the patient who effected by covid-19 or not using x- ray images. Deep learning techniques utilized to classify these images by using convolutional neural network (CNN). The dataset have been utilized in this work consist of 1000 x-ray images collected from kaggle website and divided it into 80% for training and 20% for validation. The proposed method using the pertained networks such as (EffienentNet B0, ResNet50) to minimize the training time with high performance, where the EffienentNet B0 network give high accuracy is 98.5%,finaly the model has been implemented on raspberry pi3 successfully for classification task.
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基于迁移学习技术的covid-19肺部感染病理结果分析
2019年,一种新的综合征出现在大量人群身上,如:高温、咳嗽、嗅觉和味觉丧失(迫使他们中的许多人在病毒出现后进入重症监护病房,这种综合征被命名为SARS-CoV2)。本文的目的是利用x线图像识别是否感染covid-19的患者。深度学习技术利用卷积神经网络(CNN)对这些图像进行分类。本研究使用的数据集是来自kaggle网站的1000张x射线图像,并将其分为80%用于训练,20%用于验证。该方法利用相关网络(EffienentNet B0, ResNet50),最大限度地减少了训练时间,并取得了良好的性能,其中EffienentNet B0网络的准确率高达98.5%,最终该模型已在树莓pi3上成功实现。
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