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Age‐Related Macular Degeneration Detection Using OCT Image: Deep Hybrid Classifier With Improved PCA‐Based Selective Feature Set.

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Title: Age‐Related Macular Degeneration Detection Using OCT Image: Deep Hybrid Classifier With Improved PCA‐Based Selective Feature Set.
Authors: Chaitanya, Aravapalli Sri1 (AUTHOR) chaitanyaaravapalli@gmail.com, Arunkumar, R.1 (AUTHOR), Maguluri, Lakshmana Phaneendra2 (AUTHOR)
Source: Computational Intelligence. Dec2025, Vol. 41 Issue 6, p1-23. 23p.
Subjects: Macular degeneration, Optical coherence tomography, Image segmentation, Ensemble learning, Principal components analysis, Early diagnosis, Deep learning, Feature extraction
Abstract: Age‐related macular degeneration (AMD) is a progressive retinal disease that can lead to vision loss if not diagnosed early. Accurate and timely detection is essential for effective treatment. This study presents a comprehensive deep learning‐based framework for the automated detection of AMD using optical coherence tomography (OCT) images. The proposed method integrates advanced preprocessing, segmentation, feature extraction, and classification techniques. An improved Wiener filter is applied to enhance image quality by reducing noise while preserving edge details. A U‐Net architecture is then used for accurate segmentation of retinal regions. Subsequently, diverse features such as histogram of oriented gradients (HOG), gray‐level co‐occurrence matrix (GLCM), scale‐invariant feature transform (SIFT), VGG16, statistical features, and improved median ternary pattern (MTP) are extracted from segmented images. Then, an enhanced principal component analysis (PCA) with feature scaling is used for the optimal selection of features to improve classification robustness. Finally, a hybrid model combining deep convolutional neural networks (DCNNs) and AlexNet is proposed for AMD classification. Moreover, the proposed model is evaluated against traditional methods and achieves superior performance, with an accuracy of 0.958 and F‐measure of 0.964. Experimental results confirm the model's effectiveness and reliability, highlighting its potential for clinical application in early AMD detection and diagnosis. [ABSTRACT FROM AUTHOR]
Copyright of Computational Intelligence is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Age‐Related Macular Degeneration Detection Using OCT Image: Deep Hybrid Classifier With Improved PCA‐Based Selective Feature Set.
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  Data: <searchLink fieldCode="JN" term="%22Computational+Intelligence%22">Computational Intelligence</searchLink>. Dec2025, Vol. 41 Issue 6, p1-23. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Macular+degeneration%22">Macular degeneration</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+coherence+tomography%22">Optical coherence tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Early+diagnosis%22">Early diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink>
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  Data: Age‐related macular degeneration (AMD) is a progressive retinal disease that can lead to vision loss if not diagnosed early. Accurate and timely detection is essential for effective treatment. This study presents a comprehensive deep learning‐based framework for the automated detection of AMD using optical coherence tomography (OCT) images. The proposed method integrates advanced preprocessing, segmentation, feature extraction, and classification techniques. An improved Wiener filter is applied to enhance image quality by reducing noise while preserving edge details. A U‐Net architecture is then used for accurate segmentation of retinal regions. Subsequently, diverse features such as histogram of oriented gradients (HOG), gray‐level co‐occurrence matrix (GLCM), scale‐invariant feature transform (SIFT), VGG16, statistical features, and improved median ternary pattern (MTP) are extracted from segmented images. Then, an enhanced principal component analysis (PCA) with feature scaling is used for the optimal selection of features to improve classification robustness. Finally, a hybrid model combining deep convolutional neural networks (DCNNs) and AlexNet is proposed for AMD classification. Moreover, the proposed model is evaluated against traditional methods and achieves superior performance, with an accuracy of 0.958 and F‐measure of 0.964. Experimental results confirm the model's effectiveness and reliability, highlighting its potential for clinical application in early AMD detection and diagnosis. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computational Intelligence is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/coin.70142
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      – Code: eng
        Text: English
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        PageCount: 23
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      – SubjectFull: Macular degeneration
        Type: general
      – SubjectFull: Optical coherence tomography
        Type: general
      – SubjectFull: Image segmentation
        Type: general
      – SubjectFull: Ensemble learning
        Type: general
      – SubjectFull: Principal components analysis
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      – SubjectFull: Early diagnosis
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Feature extraction
        Type: general
    Titles:
      – TitleFull: Age‐Related Macular Degeneration Detection Using OCT Image: Deep Hybrid Classifier With Improved PCA‐Based Selective Feature Set.
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            NameFull: Chaitanya, Aravapalli Sri
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            NameFull: Arunkumar, R.
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            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
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            – TitleFull: Computational Intelligence
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