Early detection of diabetic retinopathy using deep learning techniques
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
| Main Authors: | , , , |
|---|---|
| Outros autores: | |
| Formato: | Thesis |
| Idioma: | English |
| Publicado: |
Brac University
2022
|
| Subjects: | |
| Acceso en liña: | http://hdl.handle.net/10361/15869 |
| id |
10361-15869 |
|---|---|
| record_format |
dspace |
| spelling |
10361-158692022-01-26T10:21:49Z Early detection of diabetic retinopathy using deep learning techniques Gomes, Veronica Jessica Alavee, Kazi Ahnaf Sarda, Anirudh Akhand, Zebel-E-Noor Mostakim, Moin Parvez, Mohammad Zavid Department of Computer Science and Engineering, Brac University Data preprocessing Transfer learning Convolutional neural network Xception Inception Cognitive learning theory (Deep learning) Diabetic retinopathy Electronic data processing--Data preparation 286 906984 Engineering This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 33-35). We, humans, are the bearer of diseases. While most of them have a thoroughly researched and contemplated solution set, some of them do not. Diabetes is one of those common diseases that do not have a clear solution but has ways to minimize its e ects. It is a globally prevalent condition that leads to several complications in- cluding those that are deadly. One of those intricate complexities includes Diabetic retinopathy (DR), a human eye disease that may a ect one or both eyes hamper- ing the functionality and leading to compromised vision and eventually, permanent blindness. Thus, detection of diabetic retinopathy in the primitive stages will help reduce the chances of getting visually impaired, following proper treatment and other necessary precautions. The prime objective of our paper is to take aid from the state-of-the-art models which are pretrained on di erent images and also to pro- pose a basic CNN model that will have comparative results. To be more precise, we have used transfer learning models like DenseNet121, Xception, Resnet50, VGG16, VGG19, and Inception to classify the data based on single-label and multi-label. In our approach, single-label classi cation using categorical cross-entropy and softmax function works better as we reached the best accuracy, precision, and recall values using the approach. In our case, Xception has reached an accuracy of 82% which is a state-of-the-art result for the used dataset. In addition, our proposed model reached an accuracy of 71% which worked better than some of the transfer learning models. Finally, most of our approaches classi ed the data correctly even though the dataset is very unevenly distributed. Veronica Jessica Gomes Kazi Ahnaf Alavee Anirudh Sarda Zebel-E-Noor Akhand B. Computer Science 2022-01-12T06:00:18Z 2022-01-12T06:00:18Z 2021 2021-10 Thesis ID 20241053 ID 17241013 ID 21341051 ID 17201124 http://hdl.handle.net/10361/15869 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. 35 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Data preprocessing Transfer learning Convolutional neural network Xception Inception Cognitive learning theory (Deep learning) Diabetic retinopathy Electronic data processing--Data preparation 286 906984 Engineering |
| spellingShingle |
Data preprocessing Transfer learning Convolutional neural network Xception Inception Cognitive learning theory (Deep learning) Diabetic retinopathy Electronic data processing--Data preparation 286 906984 Engineering Gomes, Veronica Jessica Alavee, Kazi Ahnaf Sarda, Anirudh Akhand, Zebel-E-Noor Early detection of diabetic retinopathy using deep learning techniques |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Mostakim, Moin |
| author_facet |
Mostakim, Moin Gomes, Veronica Jessica Alavee, Kazi Ahnaf Sarda, Anirudh Akhand, Zebel-E-Noor |
| format |
Thesis |
| author |
Gomes, Veronica Jessica Alavee, Kazi Ahnaf Sarda, Anirudh Akhand, Zebel-E-Noor |
| author_sort |
Gomes, Veronica Jessica |
| title |
Early detection of diabetic retinopathy using deep learning techniques |
| title_short |
Early detection of diabetic retinopathy using deep learning techniques |
| title_full |
Early detection of diabetic retinopathy using deep learning techniques |
| title_fullStr |
Early detection of diabetic retinopathy using deep learning techniques |
| title_full_unstemmed |
Early detection of diabetic retinopathy using deep learning techniques |
| title_sort |
early detection of diabetic retinopathy using deep learning techniques |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/15869 |
| work_keys_str_mv |
AT gomesveronicajessica earlydetectionofdiabeticretinopathyusingdeeplearningtechniques AT alaveekaziahnaf earlydetectionofdiabeticretinopathyusingdeeplearningtechniques AT sardaanirudh earlydetectionofdiabeticretinopathyusingdeeplearningtechniques AT akhandzebelenoor earlydetectionofdiabeticretinopathyusingdeeplearningtechniques |
| _version_ |
1814309500650258432 |