Deep convolutional GAN-based data augmentation for medical image classification
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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| Формат: | Диссертация |
| Язык: | English |
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Brac University
2022
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| Online-ссылка: | http://hdl.handle.net/10361/17177 |
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10361-171772022-09-08T21:01:37Z Deep convolutional GAN-based data augmentation for medical image classification Datta, Joy Durdana, Bedria Rafi, Salwa Mostakim, Moin Department of Computer Science and Engineering, Brac University Data augmentation DCGAN Deep learning Classification MRI Natural language processing (Computer science) Machine learning 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 30-32). The field of medical imaging is rapidly growing with the help of machine learning, yet the problem of scarcity in labeled medical imaging still remains. Therefore training a machine learning model for medical image processing is always a difficult task. Data scarcity can be solved by using data augmentation techniques which produce and add additional data to the existing dataset. Importance of an augmented dataset also includes increasing model prediction accuracy, adding more training data to models, reducing data overfitting and creating variability in data, increasing generalization ability of models, resolving class imbalance issues in classification, and lowering data collection and labeling costs. It also helps train convolutional neural networks for increased average accuracy. This paper focuses on solving data deficiency in medical imaging through the use of an MRI dataset based on Alzheimer’s affected patients. It accomplishes this by employing deep convolutional generative adversarial networks (DCGAN) for generating realistic samples from the dataset. Other approaches for making convincing new images from labeled original images differ from using a deep convolutional generative adversarial network. DCGAN learns from training samples and can generate realistic imaging data with a similar variations, distinct from the original data. We chose to further Alzheimer’s research because, like most neurodegenerative disorders, the clinical diagnosis of Alzheimer’s dementia had a sensitivity of 71% to 87% and a specificity of 44% to 71%, implying high rates of Alzheimer’s Disease misdiagnosis among patients with cognitive impairment. Considering that alarming rate, early diagnosis of Alzheimer’s disease necessitates the use of effective automated approaches. Joy Datta Bedria Durdana Salwa Rafi B. Computer Science 2022-09-08T04:49:31Z 2022-09-08T04:49:31Z 2022 2022-01 Thesis ID 17301051 ID 17341004 ID 19241010 http://hdl.handle.net/10361/17177 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. 32 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Data augmentation DCGAN Deep learning Classification MRI Natural language processing (Computer science) Machine learning |
| spellingShingle |
Data augmentation DCGAN Deep learning Classification MRI Natural language processing (Computer science) Machine learning Datta, Joy Durdana, Bedria Rafi, Salwa Deep convolutional GAN-based data augmentation for medical image classification |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Mostakim, Moin |
| author_facet |
Mostakim, Moin Datta, Joy Durdana, Bedria Rafi, Salwa |
| format |
Thesis |
| author |
Datta, Joy Durdana, Bedria Rafi, Salwa |
| author_sort |
Datta, Joy |
| title |
Deep convolutional GAN-based data augmentation for medical image classification |
| title_short |
Deep convolutional GAN-based data augmentation for medical image classification |
| title_full |
Deep convolutional GAN-based data augmentation for medical image classification |
| title_fullStr |
Deep convolutional GAN-based data augmentation for medical image classification |
| title_full_unstemmed |
Deep convolutional GAN-based data augmentation for medical image classification |
| title_sort |
deep convolutional gan-based data augmentation for medical image classification |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17177 |
| work_keys_str_mv |
AT dattajoy deepconvolutionalganbaseddataaugmentationformedicalimageclassification AT durdanabedria deepconvolutionalganbaseddataaugmentationformedicalimageclassification AT rafisalwa deepconvolutionalganbaseddataaugmentationformedicalimageclassification |
| _version_ |
1814307806125228032 |