Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach
Objective Our objective is to develop a novel keratoconus image classification system that leverages multiple pretrained models and a transformer architecture to achieve state-of-the-art performance in detecting keratoconus.Methods and analysis Three pretrained models were used to extract features f...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2024-05-01
|
| Series: | BMJ Open Ophthalmology |
| Online Access: | https://bmjophth.bmj.com/content/9/1/e001589.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832086498399223808 |
|---|---|
| author | Shokufeh Yaraghi Toktam Khatibi |
| author_facet | Shokufeh Yaraghi Toktam Khatibi |
| author_sort | Shokufeh Yaraghi |
| collection | DOAJ |
| description | Objective Our objective is to develop a novel keratoconus image classification system that leverages multiple pretrained models and a transformer architecture to achieve state-of-the-art performance in detecting keratoconus.Methods and analysis Three pretrained models were used to extract features from the input images. These models have been trained on large datasets and have demonstrated strong performance in various computer vision tasks.The extracted features from the three pretrained models were fused using a feature fusion technique. This fusion aimed to combine the strengths of each model and capture a more comprehensive representation of the input images. The fused features were then used as input to a vision transformer, a powerful architecture that has shown excellent performance in image classification tasks. The vision transformer learnt to classify the input images as either indicative of keratoconus or not.The proposed method was applied to the Shahroud Cohort Eye collection and keratoconus detection dataset. The performance of the model was evaluated using standard evaluation metrics such as accuracy, precision, recall and F1 score.Results The research results demonstrated that the proposed model achieved higher accuracy compared with using each model individually.Conclusion The findings of this study suggest that the proposed approach can significantly improve the accuracy of image classification models for keratoconus detection. This approach can serve as an effective decision support system alongside physicians, aiding in the diagnosis of keratoconus and potentially reducing the need for invasive procedures such as corneal transplantation in severe cases. |
| format | Article |
| id | doaj-art-49654c358e3d4419906c858cc76acf69 |
| institution | Kabale University |
| issn | 2397-3269 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open Ophthalmology |
| spelling | doaj-art-49654c358e3d4419906c858cc76acf692025-02-06T11:50:10ZengBMJ Publishing GroupBMJ Open Ophthalmology2397-32692024-05-019110.1136/bmjophth-2023-001589Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approachShokufeh Yaraghi0Toktam Khatibi1Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran (the Islamic Republic of)Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran (the Islamic Republic of)Objective Our objective is to develop a novel keratoconus image classification system that leverages multiple pretrained models and a transformer architecture to achieve state-of-the-art performance in detecting keratoconus.Methods and analysis Three pretrained models were used to extract features from the input images. These models have been trained on large datasets and have demonstrated strong performance in various computer vision tasks.The extracted features from the three pretrained models were fused using a feature fusion technique. This fusion aimed to combine the strengths of each model and capture a more comprehensive representation of the input images. The fused features were then used as input to a vision transformer, a powerful architecture that has shown excellent performance in image classification tasks. The vision transformer learnt to classify the input images as either indicative of keratoconus or not.The proposed method was applied to the Shahroud Cohort Eye collection and keratoconus detection dataset. The performance of the model was evaluated using standard evaluation metrics such as accuracy, precision, recall and F1 score.Results The research results demonstrated that the proposed model achieved higher accuracy compared with using each model individually.Conclusion The findings of this study suggest that the proposed approach can significantly improve the accuracy of image classification models for keratoconus detection. This approach can serve as an effective decision support system alongside physicians, aiding in the diagnosis of keratoconus and potentially reducing the need for invasive procedures such as corneal transplantation in severe cases.https://bmjophth.bmj.com/content/9/1/e001589.full |
| spellingShingle | Shokufeh Yaraghi Toktam Khatibi Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach BMJ Open Ophthalmology |
| title | Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach |
| title_full | Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach |
| title_fullStr | Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach |
| title_full_unstemmed | Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach |
| title_short | Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach |
| title_sort | keratoconus disease classification with multimodel fusion and vision transformer a pretrained model approach |
| url | https://bmjophth.bmj.com/content/9/1/e001589.full |
| work_keys_str_mv | AT shokufehyaraghi keratoconusdiseaseclassificationwithmultimodelfusionandvisiontransformerapretrainedmodelapproach AT toktamkhatibi keratoconusdiseaseclassificationwithmultimodelfusionandvisiontransformerapretrainedmodelapproach |