Detection of proliferative diabetic retinopathy in fundus images using convolution neural network
Convolution Neural Network (CNN) is one of the techniques under Artificial Neural Network (ANN) used to develop a Deep Learning Neural Network (DLNN) algorithm for detection of Proliferative Diabetic Retinopathy (PDR) on the fundus images. About 116 PDR and 150 Non-Proliferative Diabetic Retinopathy...
| Main Authors: | , , , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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Institute of Physics Publishing
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37364/ http://umpir.ump.edu.my/id/eprint/37364/1/Detection%20of%20proliferative%20diabetic%20retinopathy%20in%20fundus%20images%20using%20convolution%20neural%20network.pdf |
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| author | Hasliza, Abu Hassan Marzuqi, Yaakob Sasni, Ismail Juwairiyyah, Abd Rahman Izyani, Mat Rusni Azlee, Zabidi Ihsan, Mohd Yassin Nooritawati, Md Tahir Suraiya, Mohamad Shafie |
| author_facet | Hasliza, Abu Hassan Marzuqi, Yaakob Sasni, Ismail Juwairiyyah, Abd Rahman Izyani, Mat Rusni Azlee, Zabidi Ihsan, Mohd Yassin Nooritawati, Md Tahir Suraiya, Mohamad Shafie |
| author_sort | Hasliza, Abu Hassan |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Convolution Neural Network (CNN) is one of the techniques under Artificial Neural Network (ANN) used to develop a Deep Learning Neural Network (DLNN) algorithm for detection of Proliferative Diabetic Retinopathy (PDR) on the fundus images. About 116 PDR and 150 Non-Proliferative Diabetic Retinopathy (NPDR) of fundus images retrieved from the publicly available MESSIDOR database applied in this research. This study consisted three objectives that included the execution of two pre-processing techniques on the data-set which were resizing and normalizing the fundus images, developed deep learning operational Artificial Intelligence (AI) network of feature extraction algorithm for detection of PDR on the fundus images and determined the output classification of the network encompassing the accuracy, sensitivity and specificity. There were five different parameters carried out along this research. Here, Parameter 5 showed the best performance among the five parameters based on the value of accuracy, sensitivity, and specificity that was 73.81%, 76%, and 69% respectively. |
| first_indexed | 2025-11-15T03:25:39Z |
| format | Conference or Workshop Item |
| id | ump-37364 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:25:39Z |
| publishDate | 2020 |
| publisher | Institute of Physics Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-373642023-08-16T04:16:34Z http://umpir.ump.edu.my/id/eprint/37364/ Detection of proliferative diabetic retinopathy in fundus images using convolution neural network Hasliza, Abu Hassan Marzuqi, Yaakob Sasni, Ismail Juwairiyyah, Abd Rahman Izyani, Mat Rusni Azlee, Zabidi Ihsan, Mohd Yassin Nooritawati, Md Tahir Suraiya, Mohamad Shafie QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Convolution Neural Network (CNN) is one of the techniques under Artificial Neural Network (ANN) used to develop a Deep Learning Neural Network (DLNN) algorithm for detection of Proliferative Diabetic Retinopathy (PDR) on the fundus images. About 116 PDR and 150 Non-Proliferative Diabetic Retinopathy (NPDR) of fundus images retrieved from the publicly available MESSIDOR database applied in this research. This study consisted three objectives that included the execution of two pre-processing techniques on the data-set which were resizing and normalizing the fundus images, developed deep learning operational Artificial Intelligence (AI) network of feature extraction algorithm for detection of PDR on the fundus images and determined the output classification of the network encompassing the accuracy, sensitivity and specificity. There were five different parameters carried out along this research. Here, Parameter 5 showed the best performance among the five parameters based on the value of accuracy, sensitivity, and specificity that was 73.81%, 76%, and 69% respectively. Institute of Physics Publishing 2020-06-05 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/37364/1/Detection%20of%20proliferative%20diabetic%20retinopathy%20in%20fundus%20images%20using%20convolution%20neural%20network.pdf Hasliza, Abu Hassan and Marzuqi, Yaakob and Sasni, Ismail and Juwairiyyah, Abd Rahman and Izyani, Mat Rusni and Azlee, Zabidi and Ihsan, Mohd Yassin and Nooritawati, Md Tahir and Suraiya, Mohamad Shafie (2020) Detection of proliferative diabetic retinopathy in fundus images using convolution neural network. In: IOP Conference Series: Materials Science and Engineering; 6th International Conference on Software Engineering and Computer Systems, ICSECS 2019 , 25-27 September 2019 , Kuantan, Pahang. pp. 1-16., 769 (012029). ISSN 1757-8981 (Published) https://doi.org/10.1088/1757-899X/769/1/012029 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Hasliza, Abu Hassan Marzuqi, Yaakob Sasni, Ismail Juwairiyyah, Abd Rahman Izyani, Mat Rusni Azlee, Zabidi Ihsan, Mohd Yassin Nooritawati, Md Tahir Suraiya, Mohamad Shafie Detection of proliferative diabetic retinopathy in fundus images using convolution neural network |
| title | Detection of proliferative diabetic retinopathy in fundus images using convolution neural network |
| title_full | Detection of proliferative diabetic retinopathy in fundus images using convolution neural network |
| title_fullStr | Detection of proliferative diabetic retinopathy in fundus images using convolution neural network |
| title_full_unstemmed | Detection of proliferative diabetic retinopathy in fundus images using convolution neural network |
| title_short | Detection of proliferative diabetic retinopathy in fundus images using convolution neural network |
| title_sort | detection of proliferative diabetic retinopathy in fundus images using convolution neural network |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/37364/ http://umpir.ump.edu.my/id/eprint/37364/ http://umpir.ump.edu.my/id/eprint/37364/1/Detection%20of%20proliferative%20diabetic%20retinopathy%20in%20fundus%20images%20using%20convolution%20neural%20network.pdf |