Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease Classification
Keratoconus is a progressive eye disorder that, if undetected, can lead to severe visual impairment or blindness, necessitating early and accurate diagnosis. The primary objective of this research is to develop a feature fusion hybrid deep learning framework that integrates pretrained Convolutional...
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2025-04-01
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| Series: | Emerging Science Journal |
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| Online Access: | https://ijournalse.org/index.php/ESJ/article/view/2922 |
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| author | Md Fatin Ishrak Md Maruf Rahman Md Imran Kabir Joy Anna Tamuly Salma Akter Dewan M. Tanim Shahajada Jawar Nayeem Ahmed Md Sadekur Rahman |
| author_facet | Md Fatin Ishrak Md Maruf Rahman Md Imran Kabir Joy Anna Tamuly Salma Akter Dewan M. Tanim Shahajada Jawar Nayeem Ahmed Md Sadekur Rahman |
| author_sort | Md Fatin Ishrak |
| collection | DOAJ |
| description | Keratoconus is a progressive eye disorder that, if undetected, can lead to severe visual impairment or blindness, necessitating early and accurate diagnosis. The primary objective of this research is to develop a feature fusion hybrid deep learning framework that integrates pretrained Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) for the automated classification of keratoconus into three distinct categories: Keratoconus, Normal, and Suspect. The dataset employed in this study is sourced from a widely recognized and publicly available online repository. Prior to model development, comprehensive preprocessing techniques were applied, including the removal of low-quality samples, image resizing, rescaling, and data augmentation. The dataset was subsequently partitioned into training, testing, and validation subsets to facilitate robust model training and performance evaluation. Eight state-of-the-art CNN architectures, including DenseNet121, EfficientNetB0, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50, VGG16, and VGG19, were utilized for feature extraction, while the ViT served as the classification head, leveraging its global attention mechanism for enhanced contextual learning, achieving near-perfect accuracy (e.g., DenseNet121+ViT: 99.28%). This study underscores the potential of hybrid CNN-ViT architectures to revolutionize keratoconus diagnosis, offering scalable, accurate, and efficient solutions to overcome limitations of traditional diagnostic methods while paving the way for broader applications in medical imaging.
Doi: 10.28991/ESJ-2025-09-02-027
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| format | Article |
| id | doaj-art-3e20021ba62244fb966e87f32df536df |
| institution | OA Journals |
| issn | 2610-9182 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj-art-3e20021ba62244fb966e87f32df536df2025-08-20T01:49:04ZengItal PublicationEmerging Science Journal2610-91822025-04-01921037107510.28991/ESJ-2025-09-02-027824Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease ClassificationMd Fatin Ishrak0Md Maruf Rahman1Md Imran Kabir Joy2Anna Tamuly3Salma Akter4Dewan M. Tanim5Shahajada Jawar6Nayeem Ahmed7Md Sadekur Rahman8Department of Electrical and Computer Engineering, University of Memphis, Memphis,Department of Marketing & Business Analytics, Texas A&M University- Commerce, Texas,MSA in Engineering Management, Central Michigan University, Michigan,Department of Computer Science, University of Memphis, Memphis,Department of Public Administration, Gannon University, Pennsylvania,Department of Computer and Information Science, Gannon University, Pennsylvania,Department of Computer and Information Science, Gannon University, Pennsylvania,Department of Computer Science, University of Memphis, Memphis,Department of Computer Science and Engineering, Daffodil International University, Birulia,Keratoconus is a progressive eye disorder that, if undetected, can lead to severe visual impairment or blindness, necessitating early and accurate diagnosis. The primary objective of this research is to develop a feature fusion hybrid deep learning framework that integrates pretrained Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) for the automated classification of keratoconus into three distinct categories: Keratoconus, Normal, and Suspect. The dataset employed in this study is sourced from a widely recognized and publicly available online repository. Prior to model development, comprehensive preprocessing techniques were applied, including the removal of low-quality samples, image resizing, rescaling, and data augmentation. The dataset was subsequently partitioned into training, testing, and validation subsets to facilitate robust model training and performance evaluation. Eight state-of-the-art CNN architectures, including DenseNet121, EfficientNetB0, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50, VGG16, and VGG19, were utilized for feature extraction, while the ViT served as the classification head, leveraging its global attention mechanism for enhanced contextual learning, achieving near-perfect accuracy (e.g., DenseNet121+ViT: 99.28%). This study underscores the potential of hybrid CNN-ViT architectures to revolutionize keratoconus diagnosis, offering scalable, accurate, and efficient solutions to overcome limitations of traditional diagnostic methods while paving the way for broader applications in medical imaging. Doi: 10.28991/ESJ-2025-09-02-027 Full Text: PDFhttps://ijournalse.org/index.php/ESJ/article/view/2922feature fusion modelkeratoconusvision transformerdensenet121efficientnetb0inceptionresnetv2inceptionv3mobilenetv2resnet50vgg16vgg19. |
| spellingShingle | Md Fatin Ishrak Md Maruf Rahman Md Imran Kabir Joy Anna Tamuly Salma Akter Dewan M. Tanim Shahajada Jawar Nayeem Ahmed Md Sadekur Rahman Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease Classification Emerging Science Journal feature fusion model keratoconus vision transformer densenet121 efficientnetb0 inceptionresnetv2 inceptionv3 mobilenetv2 resnet50 vgg16 vgg19. |
| title | Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease Classification |
| title_full | Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease Classification |
| title_fullStr | Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease Classification |
| title_full_unstemmed | Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease Classification |
| title_short | Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease Classification |
| title_sort | vision transformer embedded feature fusion model with pre trained transformers for keratoconus disease classification |
| topic | feature fusion model keratoconus vision transformer densenet121 efficientnetb0 inceptionresnetv2 inceptionv3 mobilenetv2 resnet50 vgg16 vgg19. |
| url | https://ijournalse.org/index.php/ESJ/article/view/2922 |
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