Browsing by Author "Faijul Abedin"

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    A Medical Community Android App, Detect COVID 19 and Pneumonia Using Deep-learning
    (North South University, 2021) Shuva Chowdhury; Istiak Ahamed Saif; Faijul Abedin; Amirul Ahsan Simon; Riasat Khan
    COVID-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%.