A Medical Community Android App, Detect COVID 19 and Pneumonia Using Deep-learning

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dc.contributor.advisorRiasat Khan
dc.contributor.authorShuva Chowdhury
dc.contributor.authorIstiak Ahamed Saif
dc.contributor.authorFaijul Abedin
dc.contributor.authorAmirul Ahsan Simon
dc.contributor.id1731245042
dc.contributor.id1712196642
dc.contributor.id1711316642
dc.contributor.id1632129642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-07-10
dc.date.accessioned2025-07-10T03:42:48Z
dc.date.available2025-07-10T03:42:48Z
dc.date.issued2021
dc.description.abstractCOVID-19 is the biggest headache for the whole world, including detecting COVID-19-affected patients. Early detection of COVID-19 may aid in the development of a treatment strategy and disease containment decisions. Also, a community through application among doctors, nurses, and patients can reduce deprivation of treatment and health care services. In this paper, we make a medical community Android application for doctors, nurses, and patients that can detect COVID-19 from chest X-ray photographs developed using a convolutional neural network deep learning algorithm (VGG16). The COVID-19, Pneumonia, and standard chest X-ray images are collected and joined from a public source, Kaggle. 9000 chest X-ray photographs were used for training, including 3000 COVID-19 chest X-ray photographs, 3000 Pneumonia chest X-ray photographs, and 3000 standard chest X-ray photographs. For testing, 3000 chest X-ray photographs were collected, with 1000 COVID-19 chest X-rays, 1000 Pneumonia chest X-rays, and 1000 normal chest X-rays. The accuracy of our training is 98 %, while the accuracy of our validation is 95%.
dc.description.degreeUndergraduate
dc.identifier.cd600000396
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1238
dc.language.isoen
dc.publisherNorth South University
dc.rights©Nsulibrary
dc.titleA Medical Community Android App, Detect COVID 19 and Pneumonia Using Deep-learning
dc.typeProject
oaire.citation.endPage23
oaire.citation.startPage1
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