A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

Bibliografiset tiedot
Päätekijät:	Chowdhury, Asif Hasan, Islam, Md. Fahim, Riad, M Ragib Anjum, Hashem, Faiyaz Bin
Muut tekijät:	Alam, Md. Golam Rabiul
Aineistotyyppi:	Opinnäyte
Kieli:	English
Julkaistu:	Brac University 2023
Aiheet:	AI VGG19 Inception V3 DenseNet201 SWIN transformer Feder- ated learning Biomedical engineering Neural networks (Computer science)
Linkit:	http://hdl.handle.net/10361/21844

id	10361-21844
record_format	dspace
spelling	10361-218442023-10-16T21:05:39Z A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN Chowdhury, Asif Hasan Islam, Md. Fahim Riad, M Ragib Anjum Hashem, Faiyaz Bin Alam, Md. Golam Rabiul Reza, Md Tanzim Department of Computer Science and Engineering, Brac University AI VGG19 Inception V3 DenseNet201 SWIN transformer Feder- ated learning Biomedical engineering Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 38-39). The significant advancements in computational power create the vast opportunity for using Artificial Intelligence in different applications of healthcare and medical science. A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging a combination of SWIN transformer and CNN is the combination of cutting-edge technology of AI and Federated Learning. Since, medical specialists and hospitals will have shared data space, based on that data, with the help of Artificial Intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports. We will use advanced and the latest available technology that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, Inception V3, VGG 19) and the Transformer model Swin Transformer in order to prepare our hybrid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this thesis, we will discuss how the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learning can ensure hybrid model security and keep the authenticity of the information. B.Sc. in Computer Science 2023-10-16T06:43:28Z 2023-10-16T06:43:28Z ©2022 2022-09-28 Thesis ID 18101278 ID 18101501 ID 18101472 ID 18101278 http://hdl.handle.net/10361/21844 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. 51 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	AI VGG19 Inception V3 DenseNet201 SWIN transformer Feder- ated learning Biomedical engineering Neural networks (Computer science)
spellingShingle	AI VGG19 Inception V3 DenseNet201 SWIN transformer Feder- ated learning Biomedical engineering Neural networks (Computer science) Chowdhury, Asif Hasan Islam, Md. Fahim Riad, M Ragib Anjum Hashem, Faiyaz Bin A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Chowdhury, Asif Hasan Islam, Md. Fahim Riad, M Ragib Anjum Hashem, Faiyaz Bin
format	Thesis
author	Chowdhury, Asif Hasan Islam, Md. Fahim Riad, M Ragib Anjum Hashem, Faiyaz Bin
author_sort	Chowdhury, Asif Hasan
title	A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN
title_short	A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN
title_full	A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN
title_fullStr	A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN
title_full_unstemmed	A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN
title_sort	hybrid fl-enabled ensemble approach for lung disease diagnosis leveraging fusion of swin transformer and cnn
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/21844
work_keys_str_mv	AT chowdhuryasifhasan ahybridflenabledensembleapproachforlungdiseasediagnosisleveragingfusionofswintransformerandcnn AT islammdfahim ahybridflenabledensembleapproachforlungdiseasediagnosisleveragingfusionofswintransformerandcnn AT riadmragibanjum ahybridflenabledensembleapproachforlungdiseasediagnosisleveragingfusionofswintransformerandcnn AT hashemfaiyazbin ahybridflenabledensembleapproachforlungdiseasediagnosisleveragingfusionofswintransformerandcnn AT chowdhuryasifhasan hybridflenabledensembleapproachforlungdiseasediagnosisleveragingfusionofswintransformerandcnn AT islammdfahim hybridflenabledensembleapproachforlungdiseasediagnosisleveragingfusionofswintransformerandcnn AT riadmragibanjum hybridflenabledensembleapproachforlungdiseasediagnosisleveragingfusionofswintransformerandcnn AT hashemfaiyazbin hybridflenabledensembleapproachforlungdiseasediagnosisleveragingfusionofswintransformerandcnn
_version_	1814309590058139648

A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN

Samankaltaisia teoksia