Retinal Diseases Detection using Deep Learning
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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Brac University
2023
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10361-179192023-02-26T21:01:41Z Retinal Diseases Detection using Deep Learning Mashfi, Shahriar Roy, Amit Abdullah, Riasat Ahmed, Fahim Khan, Sazid Hayat Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University Image Processing Computer vision CNN Image Segmentation CNV DME DRUSEN Resnet50 Inceptionv3 EfficientNet B0 Xception VGG16 Machine learning--Medical applications. 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 36-38). Retina is an important aspect of human vision because it converts light rays into images and sends messages to the brain. We run the danger of suffering long-term harm to the eyesight if we have a problem with our retina that might lead to vi sion loss or blindness which can be caused by eye illness, ocular trauma, or other problems. Retinal based diseases such as diabetic retinopathy, age-related macular degeneration (AMD) and retinal detachment . However, if someone can take care of his/her retinal health by eye-checkup annually it might help. Moreover, human civ ilization is now way advanced by the blessings of modern technology. Furthermore, we came up with an idea which will lead us to the success door of retinal disease detection in a very easy and cheap way. In this modern world, a large amount of people use smartphones and high resolution cameras and that is the main fact. De tecting retinal diseases with computer vision based image processing will help a lot of people in the world to be healthy in terms of their eyesight. We are planning to apply Convolutional Neural Network (CNN) to identify and classify retinal diseases with high accuracy. However,we will go through some methodologies such as data pre-processing, segmentation, analyzing etc. For Large-Scale Image Recognition we are using our customized Convolutional Network that we have proposed in this pa per. Here, we started our data segmentation from Kaggle. We have used 28972 images from Kaggle as our data-set. Then we segmented it in three parts: Test, training and validation. And here we will detect a total of four different retinal pictures.. They are: CNV, DME, DRUSEN and NORMAL. We have trained our proposed CNN model with these dataset and gained 98.97% validation accuracy. Moreover, we also run some pre-trained models. They are: Resnet50, Inceptionv3, EfficientNet B0, Xception and VGG16. We gained 79.34%, 91.32%, 28%, 87.94% and 94.01% accuracy respectively from them. Hence, we can see that our proposed CNN model outperformed them in these experimental results. Shahriar Mashfi Amit Roy Riasat Abdullah Fahim Ahmed Sazid Hayat Khan B. Computer Science 2023-02-26T06:09:36Z 2023-02-26T06:09:36Z 2022 2022-09 Thesis ID: 18201126 ID: 18301261 ID: 21101339 ID: 20301485 ID: 18201015 http://hdl.handle.net/10361/17919 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. 38 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Image Processing Computer vision CNN Image Segmentation CNV DME DRUSEN Resnet50 Inceptionv3 EfficientNet B0 Xception VGG16 Machine learning--Medical applications. |
| spellingShingle |
Image Processing Computer vision CNN Image Segmentation CNV DME DRUSEN Resnet50 Inceptionv3 EfficientNet B0 Xception VGG16 Machine learning--Medical applications. Mashfi, Shahriar Roy, Amit Abdullah, Riasat Ahmed, Fahim Khan, Sazid Hayat Retinal Diseases Detection using Deep Learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Karim, Dewan Ziaul |
| author_facet |
Karim, Dewan Ziaul Mashfi, Shahriar Roy, Amit Abdullah, Riasat Ahmed, Fahim Khan, Sazid Hayat |
| format |
Thesis |
| author |
Mashfi, Shahriar Roy, Amit Abdullah, Riasat Ahmed, Fahim Khan, Sazid Hayat |
| author_sort |
Mashfi, Shahriar |
| title |
Retinal Diseases Detection using Deep Learning |
| title_short |
Retinal Diseases Detection using Deep Learning |
| title_full |
Retinal Diseases Detection using Deep Learning |
| title_fullStr |
Retinal Diseases Detection using Deep Learning |
| title_full_unstemmed |
Retinal Diseases Detection using Deep Learning |
| title_sort |
retinal diseases detection using deep learning |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/17919 |
| work_keys_str_mv |
AT mashfishahriar retinaldiseasesdetectionusingdeeplearning AT royamit retinaldiseasesdetectionusingdeeplearning AT abdullahriasat retinaldiseasesdetectionusingdeeplearning AT ahmedfahim retinaldiseasesdetectionusingdeeplearning AT khansazidhayat retinaldiseasesdetectionusingdeeplearning |
| _version_ |
1814307567974744064 |