Brain hemorrhage detection using hybrid machine learning algorithm

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

書誌詳細
主要な著者:	Iqbal, Khondoker Nazia, Azad, Istinub, Emon, Md. Imdadul Haque, Amlan, Nibraj Safwan, Aporna, Amena Akter
その他の著者:	Islam, Md. Saiful
フォーマット:	学位論文
言語:	English
出版事項:	Brac University 2022
主題:	Hybrid machine learning Convolutional neural network (CNN) Multilayer Perceptron Model (MLP) Random Forest (RF) VGG-16 VGG-19 Brain hemorrhage Explainable AI Machine learning Neural networks (Computer science) Computational intelligence. Mathematical logic.
オンライン･アクセス:	http://hdl.handle.net/10361/16907

id	10361-16907
record_format	dspace
spelling	10361-169072022-06-06T21:01:46Z Brain hemorrhage detection using hybrid machine learning algorithm Iqbal, Khondoker Nazia Azad, Istinub Emon, Md. Imdadul Haque Amlan, Nibraj Safwan Aporna, Amena Akter Islam, Md. Saiful Rahman, Rafeed Department of Computer Science and Engineering, Brac University Hybrid machine learning Convolutional neural network (CNN) Multilayer Perceptron Model (MLP) Random Forest (RF) VGG-16 VGG-19 Brain hemorrhage Explainable AI Machine learning Neural networks (Computer science) Computational intelligence. Mathematical logic. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 31-33). Machine learning (ML) helps computers learn and program data without humans’ help. According to data scientists, machine learning can extract 60% high-quality information, reduce the cost up to 46%, and increase operation speed by approximately 48% [1]. Recently, there has been successful implementation of machine learning in data analysis, computer vision, computer-aided diseases (CAD), and many more fields. Machine learning is broadly used in the medical industry because of its processing power for image data and pattern recognition quality. The image processing power of machine learning can be used in medical images to classify the brain images automatically. Segmentation and classification of brain image can provide valuable information and quantitative assessment of lesions which can be used for treatment strategies and predicting patient condition (Kamnitsas et al., 2017). According to research [2], an estimated 64-74 million people in the world are affected by traumatic brain injury every year. It affects the lives of nearly every one out of six persons. In our proposed system, we will use a hybrid approach of multiple machine learning algorithms together for the classification of CT brain images and diagnose brain disorders and diseases like brain hemorrhage. Some ML algorithms such as different 3D Convolutional Neural Networks (CNN) , AlexNet, DenseNet121, GoogleNet and some other models like Multilayer Perceptron Model (MLP), Support Vector Machine (SVM) and Random Forest (RF) have been applied successfully in this field in the past. Modifying previous methods, we want to build a hybrid machine learning algorithm by combining different CNN models like VGG-16, VGG-19, Random forest and Multilayer Perceptron (MLP) classifiers for detecting brain hemorrhage. We have used the VGG-16 and VGG-19 model to derive image features from the CT brain images and Random forest classifier and MLP classifier for testing the accuracy of our model. To test the efficiency of our system, we have used CT brain image datasets from Kaggle. The CT brain imaging data will be the input of our model and our model will detect brain hemorrhage and classify them into one of six classes: Epidural, Intraparenchymal, Intraventricular, Subarachnoid, Subdural and No Hemorrhage. Using our hybrid approach the best accuracy we achieved was around 97.24% using a combined approach of VGG-16 and Multilayer Perceptron classifier. Also we used Explainable AI to explain the prediction of the hemorrhagic classes. Khondoker Nazia Iqbal Istinub Azad Md. Imdadul Haque Emon Nibraj Safwan Amlan Amena Akter Aporna B. Computer Science 2022-06-06T05:48:25Z 2022-06-06T05:48:25Z 2022 2022-01 Thesis ID 18101006 ID 18101045 ID 18101049 ID 18101596 ID 18301236 http://hdl.handle.net/10361/16907 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. 33 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Hybrid machine learning Convolutional neural network (CNN) Multilayer Perceptron Model (MLP) Random Forest (RF) VGG-16 VGG-19 Brain hemorrhage Explainable AI Machine learning Neural networks (Computer science) Computational intelligence. Mathematical logic.
spellingShingle	Hybrid machine learning Convolutional neural network (CNN) Multilayer Perceptron Model (MLP) Random Forest (RF) VGG-16 VGG-19 Brain hemorrhage Explainable AI Machine learning Neural networks (Computer science) Computational intelligence. Mathematical logic. Iqbal, Khondoker Nazia Azad, Istinub Emon, Md. Imdadul Haque Amlan, Nibraj Safwan Aporna, Amena Akter Brain hemorrhage detection using hybrid machine learning algorithm
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
author2	Islam, Md. Saiful
author_facet	Islam, Md. Saiful Iqbal, Khondoker Nazia Azad, Istinub Emon, Md. Imdadul Haque Amlan, Nibraj Safwan Aporna, Amena Akter
format	Thesis
author	Iqbal, Khondoker Nazia Azad, Istinub Emon, Md. Imdadul Haque Amlan, Nibraj Safwan Aporna, Amena Akter
author_sort	Iqbal, Khondoker Nazia
title	Brain hemorrhage detection using hybrid machine learning algorithm
title_short	Brain hemorrhage detection using hybrid machine learning algorithm
title_full	Brain hemorrhage detection using hybrid machine learning algorithm
title_fullStr	Brain hemorrhage detection using hybrid machine learning algorithm
title_full_unstemmed	Brain hemorrhage detection using hybrid machine learning algorithm
title_sort	brain hemorrhage detection using hybrid machine learning algorithm
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/16907
work_keys_str_mv	AT iqbalkhondokernazia brainhemorrhagedetectionusinghybridmachinelearningalgorithm AT azadistinub brainhemorrhagedetectionusinghybridmachinelearningalgorithm AT emonmdimdadulhaque brainhemorrhagedetectionusinghybridmachinelearningalgorithm AT amlannibrajsafwan brainhemorrhagedetectionusinghybridmachinelearningalgorithm AT apornaamenaakter brainhemorrhagedetectionusinghybridmachinelearningalgorithm
_version_	1814309787691646976

Brain hemorrhage detection using hybrid machine learning algorithm

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