FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912105444147200 |
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| author | Chakraborty, Bidisha Mitra, Shree |
| author_facet | Chakraborty, Bidisha Mitra, Shree |
| contents | In order to improve model accuracy, generalization, and class imbalance issues, this work offers a strong methodology for classifying endoscopic images. We suggest a hybrid feature extraction method that combines convolutional neural networks (CNNs), multi-layer perceptrons (MLPs), and radiomics. Rich, multi-scale feature extraction is made possible by this combination, which captures both deep and handmade representations. These features are then used by a classification head to classify diseases, producing a model with higher generalization and accuracy. In this framework we have achieved a validation accuracy of 76.2% in the capsule endoscopy video frame classification task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02637 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification Chakraborty, Bidisha Mitra, Shree Image and Video Processing Computer Vision and Pattern Recognition In order to improve model accuracy, generalization, and class imbalance issues, this work offers a strong methodology for classifying endoscopic images. We suggest a hybrid feature extraction method that combines convolutional neural networks (CNNs), multi-layer perceptrons (MLPs), and radiomics. Rich, multi-scale feature extraction is made possible by this combination, which captures both deep and handmade representations. These features are then used by a classification head to classify diseases, producing a model with higher generalization and accuracy. In this framework we have achieved a validation accuracy of 76.2% in the capsule endoscopy video frame classification task. |
| title | FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.02637 |