Pneumonia Disease detection using the convolutional neural network

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Détails bibliographiques
Auteurs principaux:	Ashrafee, Md. Iftid, Sourav, Koushik Barmon, Dolna, Mahazabin Khan, Haque, Samia
Autres auteurs:	Rasel, Annajiat Alim
Format:	Thèse
Langue:	English
Publié:	Brac University 2024
Sujets:	Deep learning Pneumonia detection Image processing Convolutional Neural Network(CNN) Neural network Neural networks (Computer science)
Accès en ligne:	http://hdl.handle.net/10361/22716

id	10361-22716
record_format	dspace
spelling	10361-227162024-05-05T21:02:23Z Pneumonia Disease detection using the convolutional neural network Ashrafee, Md. Iftid Sourav, Koushik Barmon Dolna, Mahazabin Khan Haque, Samia Rasel, Annajiat Alim Rahman, Rafeed Department of Computer Science and Engineering, Brac University Deep learning Pneumonia detection Image processing Convolutional Neural Network(CNN) Neural network Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 34-37) A bacterial illness called pneumonia causes inflammation in the air passages with one or even both lungs. The disease can range from mild to life-threatening. Diagnosing the disease at an earlier stage is crucial for the successful recovery of the patient. In this study, we analyze and compare various deep learning algorithms for lung illness identification and propose an updated model for pneumonia detection. The model is implemented to test its efficacy. The convolutional neural network is fed 5856 chest X-ray images split into 3 categories: training, test, and validation. Two chest conditions, namely pneumonia and normal, were detected and classified. The CNN model, trained with these datasets, achieved 94.66% training accuracy and 91.83% validation accuracy. Moreover, we also run some pre-trained models. They are: Resnet50, Inceptionv3, EfficientNet B0, Xception and VGG16,EfficientNet B6. We gained 68.91%, 83.71%, 62.50%, 91.35%, 90.75% and 62.50% accuracy respectively from them. Hence, We can observe that what was suggested. In these experimental results, the CNN model fared better than them. Md. Iftid Ashrafee Koushik Barmon Sourav Mahazabin Khan Dolna Samia Haque 2024-05-05T04:59:38Z 2024-05-05T04:59:38Z 2023 2023-01 Thesis ID: 19101201 ID: 18101387 ID: 19101207 ID: 19101468 http://hdl.handle.net/10361/22716 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 37 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep learning Pneumonia detection Image processing Convolutional Neural Network(CNN) Neural network Neural networks (Computer science)
spellingShingle	Deep learning Pneumonia detection Image processing Convolutional Neural Network(CNN) Neural network Neural networks (Computer science) Ashrafee, Md. Iftid Sourav, Koushik Barmon Dolna, Mahazabin Khan Haque, Samia Pneumonia Disease detection using the convolutional neural network
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Rasel, Annajiat Alim
author_facet	Rasel, Annajiat Alim Ashrafee, Md. Iftid Sourav, Koushik Barmon Dolna, Mahazabin Khan Haque, Samia
format	Thesis
author	Ashrafee, Md. Iftid Sourav, Koushik Barmon Dolna, Mahazabin Khan Haque, Samia
author_sort	Ashrafee, Md. Iftid
title	Pneumonia Disease detection using the convolutional neural network
title_short	Pneumonia Disease detection using the convolutional neural network
title_full	Pneumonia Disease detection using the convolutional neural network
title_fullStr	Pneumonia Disease detection using the convolutional neural network
title_full_unstemmed	Pneumonia Disease detection using the convolutional neural network
title_sort	pneumonia disease detection using the convolutional neural network
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22716
work_keys_str_mv	AT ashrafeemdiftid pneumoniadiseasedetectionusingtheconvolutionalneuralnetwork AT souravkoushikbarmon pneumoniadiseasedetectionusingtheconvolutionalneuralnetwork AT dolnamahazabinkhan pneumoniadiseasedetectionusingtheconvolutionalneuralnetwork AT haquesamia pneumoniadiseasedetectionusingtheconvolutionalneuralnetwork
_version_	1814308169808084992

Pneumonia Disease detection using the convolutional neural network

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