IDENTIFICATION OF COVID-19 FROM CHEST RADIOGRAPHIC  IMAGES USING DEEP LEARNING

MINICHIL, ABOYE ALEMNEH

dc.contributor.author	MINICHIL, ABOYE ALEMNEH
dc.date.accessioned	2022-03-18T06:27:55Z
dc.date.available	2022-03-18T06:27:55Z
dc.date.issued	2021-09
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/13207
dc.description.abstract	Corona virus disease (COVID-19), which is caused by the severe acute respiratory syndrome corona virus 2 (SARS-CoV-2), is an inflammatory disease that causes respiratory illness (similar to influenza) with symptoms such as cold, cough, and fever, as well as difficulty breathing in more severe cases. The goal of the research is to develop a model that can identify COVID-19. The information is gathered from hospitals. The visual data was analyzed using a deep learning approach (CNN). Preprocessing, feature extraction, and classification are the three components of the research. We normalize the image to a standard size during image processing. For feature extraction, we employ a convolutional neural network. It is used to identify and choose essential elements that contribute to the disease's symptom. We employ a convolutional neural network for classification. For classifying into a specific class (normal/no findings, pneumonia, and COVID-19), a three-way Softmax is employed. The research was carried out in Python using Keras (with TensorFlow as a backend) and evaluated on a sample image dataset obtained from Tibebe Gion Hospital. The model achieved a diagnosis accuracy of 98.35% for training and 98% for testing to identify Covid-19. This research work presented different contributions that can be further improved or implemented on the effort to detect and grade related diseases. Keywords: Covid-19, Deep Learning, CNN, Feature Learning, SoftMax	en_US
dc.language.iso	en_US	en_US
dc.subject	INFORMATION TECHNOLOGY	en_US
dc.title	IDENTIFICATION OF COVID-19 FROM CHEST RADIOGRAPHIC IMAGES USING DEEP LEARNING	en_US
dc.type	Thesis	en_US