An efficient deep learning approach to detect retinal disease using optical coherence tomographic images
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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| Język: | English |
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Brac University
2022
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| Dostęp online: | http://hdl.handle.net/10361/17570 |
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10361-175702022-11-15T21:01:45Z An efficient deep learning approach to detect retinal disease using optical coherence tomographic images Khan, Farhan Sakib Ferdaus, Nowshin Hossain, Tamim Islam, Quazi Sabrina Islam, Md. Iftakharul Alam, Md. Ashraful Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University Convolutional neural network Optical coherence tomography Deep learning Retinal Disease VGG16 VGG19 MobNetV2 ResNet50 DenseNet121 InceptionV3 InceptionResNetV2 Neural networks (Computer science) Optical coherence tomography 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 33-36). Optical Coherence Tomography (OCT) is an effective approach for diagnosing retinal problems that can be used in combination with traditional diagnostic testing methods. We developed and implemented a deep Convolutional Neural Network (CNN) model, which has the capability to effectively identify and classify Optical Coherence Tomography (OCT) images into the following four categories: Normal, DMD, CNV, and DME. The proposed 21 layered CNN model is built with three basic layers: a convolutional layer, a pooling layer, and a fully connected layer along with dropout and dense layers. Our proposed model is able to detect and differentiate between the OCT images with a high amount of accuracy. The 21 layer proposed CNN model was used for the classification and diagnosis of retinal sickness using OCT images. To justify the efficiency of our custom CNN model, seven pre-trained CNN models (VGG16, VGG19, MobNetV2, Resnet50, DenseNet121, InceptionV3, and InceptionResNetV2) were used and testified with the same amount of dataset. In terms of the accuracy, precision, recall, and f1 score, which are all tested in this paper, the suggested CNN model along with seven other pre-trained CNN architectures perform comparable on the available dataset. The proposed model has an accuracy rate of 98.37 percent, which is greater than any of the experimental results of the CNN models utilized in this research due to the fact that the recommended model was developed. When it comes to the diagnosis of retinal problems, the CNN model that was suggested performs far better than any other model that was previously used. Farhan Sakib Khan Nowshin Ferdaus Tamim Hossain Quazi Sabrina Islam Md. Iftakharul Islam B. Computer Science and Engineering 2022-11-15T06:21:30Z 2022-11-15T06:21:30Z 2022 2022-05 Thesis ID 18301176 ID 18101113 ID 18301183 ID 19101673 ID 18301020 http://hdl.handle.net/10361/17570 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. 36 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Convolutional neural network Optical coherence tomography Deep learning Retinal Disease VGG16 VGG19 MobNetV2 ResNet50 DenseNet121 InceptionV3 InceptionResNetV2 Neural networks (Computer science) Optical coherence tomography |
| spellingShingle |
Convolutional neural network Optical coherence tomography Deep learning Retinal Disease VGG16 VGG19 MobNetV2 ResNet50 DenseNet121 InceptionV3 InceptionResNetV2 Neural networks (Computer science) Optical coherence tomography Khan, Farhan Sakib Ferdaus, Nowshin Hossain, Tamim Islam, Quazi Sabrina Islam, Md. Iftakharul An efficient deep learning approach to detect retinal disease using optical coherence tomographic images |
| 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 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Khan, Farhan Sakib Ferdaus, Nowshin Hossain, Tamim Islam, Quazi Sabrina Islam, Md. Iftakharul |
| format |
Thesis |
| author |
Khan, Farhan Sakib Ferdaus, Nowshin Hossain, Tamim Islam, Quazi Sabrina Islam, Md. Iftakharul |
| author_sort |
Khan, Farhan Sakib |
| title |
An efficient deep learning approach to detect retinal disease using optical coherence tomographic images |
| title_short |
An efficient deep learning approach to detect retinal disease using optical coherence tomographic images |
| title_full |
An efficient deep learning approach to detect retinal disease using optical coherence tomographic images |
| title_fullStr |
An efficient deep learning approach to detect retinal disease using optical coherence tomographic images |
| title_full_unstemmed |
An efficient deep learning approach to detect retinal disease using optical coherence tomographic images |
| title_sort |
efficient deep learning approach to detect retinal disease using optical coherence tomographic images |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17570 |
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