Identifying brain abnormalities using image processing and CNN models

Cataloged from PDF version of thesis.

Bibliografske podrobnosti
Main Authors:	Talukder, Ismat Shehrin, Ninty, Rifa Tasmim, Saimon, Md. Galib Hamza, Akbar, Shafkat Asif
Drugi avtorji:	Islam, Md. Saiful
Format:	Thesis
Jezik:	en_US
Izdano:	Brac University 2021
Teme:	Brain abnormality Supervised learning CNN ML Revolutionary system Neurology ResNet50 VGG16 Inception V3 Augmentation
Online dostop:	http://hdl.handle.net/10361/15104

id	10361-15104
record_format	dspace
spelling	10361-151042022-01-26T10:13:17Z Identifying brain abnormalities using image processing and CNN models Talukder, Ismat Shehrin Ninty, Rifa Tasmim Saimon, Md. Galib Hamza Akbar, Shafkat Asif Islam, Md. Saiful Department of Computer Science and Engineering, Brac University Brain abnormality Supervised learning CNN ML Revolutionary system Neurology ResNet50 VGG16 Inception V3 Augmentation Cataloged from PDF version of thesis. Includes bibliographical references (pages 33-38). This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. In a developing country like Bangladesh, it is tough to detect a brain abnormality, i.e., Pituitary tumor, Glioma, Meningioma, etc., in an early stage and treat them accordingly. In our proposed system, ML(Machine Learning) techniques under supervised learning will allow us to predict the early detection of brain diseases. We will approach by using image processing to separate the abnormal lesions from the normal ones ideally. We’ll also use CNN (Convolutional Neural Network) to stratify diﬀerent brain abnormalities. Especially when it comes to early detection of brain abnormalities, we’ll also create image classiﬁers for stratiﬁcation without human help by integrating genomic data to give them a better chance of survival through implementing a machine learning approach. This system is focused on any abnormality related to brain activity and helping the victims to recognize it. We have used 3 CNN models: ResNet50, VGG16, Inception V3, and then obtained satisfactory results. Then we also used the augmentation process in the ResNet50, VGG16, and Inception V3 models. Therefore, we got the best accuracy result in the ResNet50 model after augmentation. Our goal is to provide the proposal to the people of Bangladesh a revolutionary system that will give a plan best suited for every individual and increase the chances of survival for neurology patients to a beyond level. Ismat Shehrin Talukder Rifa Tasmim Ninty Md. Galib Hamza Saimon Shafkat Asif Akbar B. Computer Science 2021-10-03T06:34:39Z 2021-10-03T06:34:39Z 2021 2021-06 Thesis ID: 21141025 ID: 21141026 ID: 21141027 ID: 21141028 http://hdl.handle.net/10361/15104 en_US 38 Pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	en_US
topic	Brain abnormality Supervised learning CNN ML Revolutionary system Neurology ResNet50 VGG16 Inception V3 Augmentation
spellingShingle	Brain abnormality Supervised learning CNN ML Revolutionary system Neurology ResNet50 VGG16 Inception V3 Augmentation Talukder, Ismat Shehrin Ninty, Rifa Tasmim Saimon, Md. Galib Hamza Akbar, Shafkat Asif Identifying brain abnormalities using image processing and CNN models
description	Cataloged from PDF version of thesis.
author2	Islam, Md. Saiful
author_facet	Islam, Md. Saiful Talukder, Ismat Shehrin Ninty, Rifa Tasmim Saimon, Md. Galib Hamza Akbar, Shafkat Asif
format	Thesis
author	Talukder, Ismat Shehrin Ninty, Rifa Tasmim Saimon, Md. Galib Hamza Akbar, Shafkat Asif
author_sort	Talukder, Ismat Shehrin
title	Identifying brain abnormalities using image processing and CNN models
title_short	Identifying brain abnormalities using image processing and CNN models
title_full	Identifying brain abnormalities using image processing and CNN models
title_fullStr	Identifying brain abnormalities using image processing and CNN models
title_full_unstemmed	Identifying brain abnormalities using image processing and CNN models
title_sort	identifying brain abnormalities using image processing and cnn models
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15104
work_keys_str_mv	AT talukderismatshehrin identifyingbrainabnormalitiesusingimageprocessingandcnnmodels AT nintyrifatasmim identifyingbrainabnormalitiesusingimageprocessingandcnnmodels AT saimonmdgalibhamza identifyingbrainabnormalitiesusingimageprocessingandcnnmodels AT akbarshafkatasif identifyingbrainabnormalitiesusingimageprocessingandcnnmodels
_version_	1814308141537427456

Identifying brain abnormalities using image processing and CNN models

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