الگوریتم شبکه عصبی عمیق بهبود یافته برای شناسایی بیماری کوید-19 در اینترنت اشیا

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده ریاضی و کامپیوتر، دانشگاه شهید باهنر کرمان، کرمان، ایران.

چکیده

در این مقاله یک سیستم تشخیص خودکار موارد مبتلا به کوید-19 مبتنی بر اینترنت اشیا پیشنهاد می‌شود. در مدل پیشنهادی ابتدا با استفاده از فن‌آوری اینترنت اشیا تصاویر پزشکی مستقیم پس از مراجعه فرد مشکوک از طریق تجهیزات پزشکی مجهز به اینترنت اشیا به مخزن داده ارسال می‌شود. سپس به منظور کمک به متخصصین رادیولوژی برای تفسیر هرچه بهتر تصاویر پزشکی از چهار مدل شبکه عصبی پیچشی از پیش آموزش دیده به نام‌های InceptionResNetV2، InceptionV3، VGG19 و ResNet152 و دو مجموعه داده تصاویر پزشکی رایولوژی قفسه سینه و CT Scan در یک طبقه‌بندی سه کلاسه برای پیش‌بینی دقیق موارد مبتلا به کوید-19، افراد سالم و موارد مبتلا بیماری استفاده می‌شود. درنهایت بهترین نتیجه به دست آمده برای تصاویر CT Scan متعلق به معماری InceptionResNetV2 با دقت 99.366% و برای تصاویر رادیولوژی مربوط به معماری‌ InceptionV3 با دقت 96.943% می‌باشد. نتایج نشان می‌دهد این سیستم منجر به کاهش مراجعه روزانه به مراکز درمانی و در نتیجه کاهش فشار بر سیستم مراقبت‌های درمانی می‌شود. همچنین به متخصصین رایولوژی و کادر درمان کمک می‌کند تا هرچه سریعتر بیماری شناسایی شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Improved deep neural network algorithm for COVID-19 detection in the Internet of Things

نویسندگان [English]

  • Mohammad Mousavi
  • Soodeh Hosseini
  • Mohammad Reza Omidi
Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.
چکیده [English]

In this paper, we propose an automatic detection system for COVID-19 cases based on the Internet of Things. In the proposed model, first, using Internet of Things technology, medical images are sent directly to the data collection after the suspicious person's visit through medical equipment equipped with Internet of Things, and then, in order to help radiologists to interpret medical images better, usage has been made of four pre-trained convolutional neural network models i.e. InceptionV3, InceptionResNetV2, VGG19 and ResNet152 as well as two datasets of chest radiology medical images and CT Scan in a 3-class classification for accurate prediction of cases suffering from COVID-19, healthy people, and diseased cases. Finally, the best result for CT-Scan images is related to InceptionResNetV2 architecture with an accuracy of 99.366%, and for radiology images related to the InceptionV3 architecture, it is 96.943%. The results show that this system leads to a reduction in daily visits to medical centers and thus reduces the pressure on the medical care system. It also helps rheology specialists to identify the disease as quickly as possible.

کلیدواژه‌ها [English]

  • Image processing
  • Artificial intelligence
  • Internet of Things
  • Convolutional neural network
  • Deep learning
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