Classification system from Optical Coherence Tomography using transfer learning
This research aims to create a decision support system to identify retinal diseases using a four-class classification problem. To achieve this, the proposed system uses deep learning architecture to automatically recognize CNV, DME, and drusen from OCT images. The model employs two transfer learning...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2024-11-01
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| Series: | International Journal of Electronics and Telecommunications |
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| Online Access: | https://journals.pan.pl/Content/133212/PDF/12-4722-Asvial-sk.pdf |
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| author | Muhamad Asvial Tobias Ivandito Margogo Silalahi Muh. Asnoer Laagu |
| author_facet | Muhamad Asvial Tobias Ivandito Margogo Silalahi Muh. Asnoer Laagu |
| author_sort | Muhamad Asvial |
| collection | DOAJ |
| description | This research aims to create a decision support system to identify retinal diseases using a four-class classification problem. To achieve this, the proposed system uses deep learning architecture to automatically recognize CNV, DME, and drusen from OCT images. The model employs two transfer learning architectures with several additional layers to classify retinal diseases. The purpose of model training, validation, and testing, the experiment uses 6,000 grayscale images labeled into four classes from the OCT data set. The Inception V3 model’s proposed additional layer exhibits an increase in accuracy of 3.08% and a reduction in the loss by 0.3767. The experiment’s results indicate that the Inception V3 model achieved an accuracy rate of 99.31%, and the VGG-16 model reached 98.83%, which outperformed other deep learning models using the OCT data set. |
| format | Article |
| id | doaj-art-9edecd260f9a401cbc0a61d9ed0c8f66 |
| institution | Kabale University |
| issn | 2081-8491 2300-1933 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | International Journal of Electronics and Telecommunications |
| spelling | doaj-art-9edecd260f9a401cbc0a61d9ed0c8f662024-11-28T08:41:30ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332024-11-01vol. 70No 4871877https://doi.org/10.24425/ijet.2024.152072Classification system from Optical Coherence Tomography using transfer learningMuhamad Asvial0Tobias Ivandito Margogo Silalahi1Muh. Asnoer Laagu2University of IndonesiaUniversity of IndonesiaUniversity of JemberThis research aims to create a decision support system to identify retinal diseases using a four-class classification problem. To achieve this, the proposed system uses deep learning architecture to automatically recognize CNV, DME, and drusen from OCT images. The model employs two transfer learning architectures with several additional layers to classify retinal diseases. The purpose of model training, validation, and testing, the experiment uses 6,000 grayscale images labeled into four classes from the OCT data set. The Inception V3 model’s proposed additional layer exhibits an increase in accuracy of 3.08% and a reduction in the loss by 0.3767. The experiment’s results indicate that the Inception V3 model achieved an accuracy rate of 99.31%, and the VGG-16 model reached 98.83%, which outperformed other deep learning models using the OCT data set.https://journals.pan.pl/Content/133212/PDF/12-4722-Asvial-sk.pdfdeep learningtransfer learningoptical coherence tomographyinception v3vgg-16 |
| spellingShingle | Muhamad Asvial Tobias Ivandito Margogo Silalahi Muh. Asnoer Laagu Classification system from Optical Coherence Tomography using transfer learning International Journal of Electronics and Telecommunications deep learning transfer learning optical coherence tomography inception v3 vgg-16 |
| title | Classification system from Optical Coherence Tomography using transfer learning |
| title_full | Classification system from Optical Coherence Tomography using transfer learning |
| title_fullStr | Classification system from Optical Coherence Tomography using transfer learning |
| title_full_unstemmed | Classification system from Optical Coherence Tomography using transfer learning |
| title_short | Classification system from Optical Coherence Tomography using transfer learning |
| title_sort | classification system from optical coherence tomography using transfer learning |
| topic | deep learning transfer learning optical coherence tomography inception v3 vgg-16 |
| url | https://journals.pan.pl/Content/133212/PDF/12-4722-Asvial-sk.pdf |
| work_keys_str_mv | AT muhamadasvial classificationsystemfromopticalcoherencetomographyusingtransferlearning AT tobiasivanditomargogosilalahi classificationsystemfromopticalcoherencetomographyusingtransferlearning AT muhasnoerlaagu classificationsystemfromopticalcoherencetomographyusingtransferlearning |