DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images
Macular edema (ME) is one of the most common retinal diseases that occur as a result of the detachment of the retinal layers on the macula. This study provides computer-aided identification of ME for even small pathologies on OCT using the advantages of Deep Learning. The study aims to identify ME o...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | Engineering Science and Technology, an International Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098621002238 |
| _version_ | 1797996823163437056 |
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| author | Gokhan Altan |
| author_facet | Gokhan Altan |
| author_sort | Gokhan Altan |
| collection | DOAJ |
| description | Macular edema (ME) is one of the most common retinal diseases that occur as a result of the detachment of the retinal layers on the macula. This study provides computer-aided identification of ME for even small pathologies on OCT using the advantages of Deep Learning. The study aims to identify ME on OCT images using a lightweight explainable Convolutional neural networks (CNN) architecture by composing significant feature activation maps and reducing the trainable parameters. A CNN is an effective Deep Learning algorithm, which consists of feature learning and classification stages. The proposed model, DeepOCT, focuses on reaching high classification performances as well as popular pre-trained architectures using less feature learning and shallow dense layers in addition to visualizing the most responsible regions and pathology on feature activation maps. The DeepOCT encapsulates the block-matching and 3D filtering (BM3D) algorithm, flattening the retinal layers to avoid the effects arising from different macula positions, and excluding non-retinal layers by cropping. DeepOCT identified OCT with ME with the rates of 99.20%, 100%, and 98.40% for accuracy, sensitivity, and specificity, respectively. The DeepOCT provides a standardized analysis, a lightweight architecture by reducing the number of trainable parameters, and high classification performances for both large- and small-scale datasets. It can analyze medical images at different levels with simple feature learning, whereas the literature uses complicated pre-trained feature learning architectures. |
| first_indexed | 2024-04-11T10:23:33Z |
| format | Article |
| id | doaj.art-3c637fd857a8477ca0f6099f9c32f524 |
| institution | Directory Open Access Journal |
| issn | 2215-0986 |
| language | English |
| last_indexed | 2024-04-11T10:23:33Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj.art-3c637fd857a8477ca0f6099f9c32f5242022-12-22T04:29:40ZengElsevierEngineering Science and Technology, an International Journal2215-09862022-10-0134101091DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT imagesGokhan Altan0Iskenderun Technical University, Central Campus, Block A, Computer Engineering Dept., İskenderun, Hatay, Turkey; Corresponding author.Macular edema (ME) is one of the most common retinal diseases that occur as a result of the detachment of the retinal layers on the macula. This study provides computer-aided identification of ME for even small pathologies on OCT using the advantages of Deep Learning. The study aims to identify ME on OCT images using a lightweight explainable Convolutional neural networks (CNN) architecture by composing significant feature activation maps and reducing the trainable parameters. A CNN is an effective Deep Learning algorithm, which consists of feature learning and classification stages. The proposed model, DeepOCT, focuses on reaching high classification performances as well as popular pre-trained architectures using less feature learning and shallow dense layers in addition to visualizing the most responsible regions and pathology on feature activation maps. The DeepOCT encapsulates the block-matching and 3D filtering (BM3D) algorithm, flattening the retinal layers to avoid the effects arising from different macula positions, and excluding non-retinal layers by cropping. DeepOCT identified OCT with ME with the rates of 99.20%, 100%, and 98.40% for accuracy, sensitivity, and specificity, respectively. The DeepOCT provides a standardized analysis, a lightweight architecture by reducing the number of trainable parameters, and high classification performances for both large- and small-scale datasets. It can analyze medical images at different levels with simple feature learning, whereas the literature uses complicated pre-trained feature learning architectures.http://www.sciencedirect.com/science/article/pii/S2215098621002238Deep learningConvolutional neural networksOptical coherence tomographyMacular edemaDeepOCT |
| spellingShingle | Gokhan Altan DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images Engineering Science and Technology, an International Journal Deep learning Convolutional neural networks Optical coherence tomography Macular edema DeepOCT |
| title | DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images |
| title_full | DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images |
| title_fullStr | DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images |
| title_full_unstemmed | DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images |
| title_short | DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images |
| title_sort | deepoct an explainable deep learning architecture to analyze macular edema on oct images |
| topic | Deep learning Convolutional neural networks Optical coherence tomography Macular edema DeepOCT |
| url | http://www.sciencedirect.com/science/article/pii/S2215098621002238 |
| work_keys_str_mv | AT gokhanaltan deepoctanexplainabledeeplearningarchitecturetoanalyzemacularedemaonoctimages |