An efficient deep learning approach to predict heart failure from image data using ejection fraction
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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| Lenguaje: | English |
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Brac University
2023
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| Acceso en línea: | http://hdl.handle.net/10361/21837 |
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10361-218372023-10-16T21:04:27Z An efficient deep learning approach to predict heart failure from image data using ejection fraction Tanvir, Nazmul Karim Yeasin, MD. Nadim MD. Mahmuduzzaman, Sarker Ara, Jannat Alam, Md. Ashraful Reza, Md Tanzim Department of Computer Science and Engineering, Brac University Cardiovascular Disease (CVDs) Ejection fraction (EF) CNN Heart failure (HF) Vgg-19 Vgg-16 Inception-V3 Cardiac MRI data Health informatics Optical data processing 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 29-31). Heart is the core of human body. A normal heart beats almost 1,15,200 times in a day and 80 beats per second to make us live alive. But we often take it granted and do uncertain thinks which stops it to function perfectly. In today’s world cardiovascular diseases(CVDs) almost kill 17-18 million life’s each year worldwide which makes it the biggest disease of death. If early detection of heart malfunction or Heart failure(HF) can be detect millions of people will able to breath even longer than usual. In our research our main aim is to create an automated Deep Learning based model which will predict HF and the depth of the condition. Moreover, using which type of cardiac MRI image slice we can get better result will be consider to be our main research goal. For this we choose a cardiac MRI dataset which consists of 1100 different heart patients image having different slices in different pattern. Furthermore, with more observation and leveling different parameter with the help of Ejection Fraction(EF) values which depends on systole diastole value of heart we able to predict the heart failure with an efficient result. AI, ML & deep learning is the new trend for solving real life human problems. We used different Convolution Neural Network architecture and obtained accuracy are VGG-16(88.15%), VGG- 19(87.93%), ResNet-50 (75.85%), ResNet-101 (79.53%) Inception-V3 (85.27%). Our model is being used to find the suitable result to detect the Heart Failure(HF) with Ejection Fraction(EF). Nazmul Karim Tanvir MD. Nadim Yeasin Sarker MD. Mahmuduzzaman Jannat Ara B.Sc. in Computer Science 2023-10-16T05:10:50Z 2023-10-16T05:10:50Z ©2022 2022-09-28 Thesis ID 18101054 ID 18101560 ID 18301073 ID 17301065 http://hdl.handle.net/10361/21837 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. 41 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Cardiovascular Disease (CVDs) Ejection fraction (EF) CNN Heart failure (HF) Vgg-19 Vgg-16 Inception-V3 Cardiac MRI data Health informatics Optical data processing |
| spellingShingle |
Cardiovascular Disease (CVDs) Ejection fraction (EF) CNN Heart failure (HF) Vgg-19 Vgg-16 Inception-V3 Cardiac MRI data Health informatics Optical data processing Tanvir, Nazmul Karim Yeasin, MD. Nadim MD. Mahmuduzzaman, Sarker Ara, Jannat An efficient deep learning approach to predict heart failure from image data using ejection fraction |
| 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. Ashraful |
| author_facet |
Alam, Md. Ashraful Tanvir, Nazmul Karim Yeasin, MD. Nadim MD. Mahmuduzzaman, Sarker Ara, Jannat |
| format |
Thesis |
| author |
Tanvir, Nazmul Karim Yeasin, MD. Nadim MD. Mahmuduzzaman, Sarker Ara, Jannat |
| author_sort |
Tanvir, Nazmul Karim |
| title |
An efficient deep learning approach to predict heart failure from image data using ejection fraction |
| title_short |
An efficient deep learning approach to predict heart failure from image data using ejection fraction |
| title_full |
An efficient deep learning approach to predict heart failure from image data using ejection fraction |
| title_fullStr |
An efficient deep learning approach to predict heart failure from image data using ejection fraction |
| title_full_unstemmed |
An efficient deep learning approach to predict heart failure from image data using ejection fraction |
| title_sort |
efficient deep learning approach to predict heart failure from image data using ejection fraction |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21837 |
| work_keys_str_mv |
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