Artificially intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysis
Image segmentation is a major and crucial problem in medical imaging. It is the foundation for clinical diagnostic methods, treatments, and computer-assisted disease diagnosis (CAD). An ocular condition known as retinal pigmentosa (RP) first results in night blindness and persistent degradation of t...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025003482 |
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| author | Syed Muhammad Ali Imran Abida Hussain Nema Salem Muhammad Arsalan |
| author_facet | Syed Muhammad Ali Imran Abida Hussain Nema Salem Muhammad Arsalan |
| author_sort | Syed Muhammad Ali Imran |
| collection | DOAJ |
| description | Image segmentation is a major and crucial problem in medical imaging. It is the foundation for clinical diagnostic methods, treatments, and computer-assisted disease diagnosis (CAD). An ocular condition known as retinal pigmentosa (RP) first results in night blindness and persistent degradation of the retinal pigment. An advanced deep learning method for computer-aided disease diagnosis that detects pigment signs (PS) in RP patients, enabling ophthalmologists to identify and treat the condition on or before schedule. Many researchers now use costly CAD approaches; however, the suggested solution, which uses a fundus image dataset, is quick, easy, and affordable. This paper presents a large kernel pigment sign semantic segmentation network (P3S-Net) to enhance segmentation performance and provide a useful receptive field. P3S-Net is a residual network with less trainable parameters due to its large kernel architecture. Moreover, these large-kernel receptive fields yield improved segmentation results with early, intermediate, and late feature information fusion. P3S-Net has an order of magnitude less trainable parameters of 155k in certain scenarios and is smaller than current medical image segmentation techniques. Utilizing the publicly available Retinal Images for Pigment Signs (RIPS) dataset for retinal pigment detection and segmentation, 4-fold cross-validation tests were conducted to assess the suggested P3S-Net. The performance of the suggested network is frequently better and more competitive when compared to cutting-edge techniques. |
| format | Article |
| id | doaj-art-52679df3b3ef4834b3cc4a65b9e68240 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-52679df3b3ef4834b3cc4a65b9e682402025-02-09T05:01:06ZengElsevierResults in Engineering2590-12302025-03-0125104263Artificially intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysisSyed Muhammad Ali Imran0Abida Hussain1Nema Salem2Muhammad Arsalan3Faculty of CS and IT, Superior University, 54000, Lahore, Pakistan; Intelligent Data Visual Computing Research (IDVCR), Lahore, 54600, PakistanFaculty of CS and IT, Superior University, 54000, Lahore, Pakistan; Intelligent Data Visual Computing Research (IDVCR), Lahore, 54600, PakistanElectrical Engineering Research Laboratory, Electrical and Computer Engineering Department, College of Engineering, Effat University, Jeddah, Kingdom of Saudi Arabia; Corresponding authors.College of Engineering, Qatar University, 2713, Doha, Qatar; Corresponding authors.Image segmentation is a major and crucial problem in medical imaging. It is the foundation for clinical diagnostic methods, treatments, and computer-assisted disease diagnosis (CAD). An ocular condition known as retinal pigmentosa (RP) first results in night blindness and persistent degradation of the retinal pigment. An advanced deep learning method for computer-aided disease diagnosis that detects pigment signs (PS) in RP patients, enabling ophthalmologists to identify and treat the condition on or before schedule. Many researchers now use costly CAD approaches; however, the suggested solution, which uses a fundus image dataset, is quick, easy, and affordable. This paper presents a large kernel pigment sign semantic segmentation network (P3S-Net) to enhance segmentation performance and provide a useful receptive field. P3S-Net is a residual network with less trainable parameters due to its large kernel architecture. Moreover, these large-kernel receptive fields yield improved segmentation results with early, intermediate, and late feature information fusion. P3S-Net has an order of magnitude less trainable parameters of 155k in certain scenarios and is smaller than current medical image segmentation techniques. Utilizing the publicly available Retinal Images for Pigment Signs (RIPS) dataset for retinal pigment detection and segmentation, 4-fold cross-validation tests were conducted to assess the suggested P3S-Net. The performance of the suggested network is frequently better and more competitive when compared to cutting-edge techniques.http://www.sciencedirect.com/science/article/pii/S2590123025003482SDG 3Deep learningComputer-aided diagnosis (CAD)Fundus imagesMedical image segmentationPigment signs semantic segmentation |
| spellingShingle | Syed Muhammad Ali Imran Abida Hussain Nema Salem Muhammad Arsalan Artificially intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysis Results in Engineering SDG 3 Deep learning Computer-aided diagnosis (CAD) Fundus images Medical image segmentation Pigment signs semantic segmentation |
| title | Artificially intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysis |
| title_full | Artificially intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysis |
| title_fullStr | Artificially intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysis |
| title_full_unstemmed | Artificially intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysis |
| title_short | Artificially intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysis |
| title_sort | artificially intelligent detection of retinal pigment sign using p3s net for retinitis pigmentosa analysis |
| topic | SDG 3 Deep learning Computer-aided diagnosis (CAD) Fundus images Medical image segmentation Pigment signs semantic segmentation |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025003482 |
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