FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification
This work addresses disease classification in medical imaging for capsule endoscopy, but it appears incremental as it builds on existing methods.
The paper tackled the problem of classifying capsule endoscopy images by proposing a hybrid feature fusion framework combining CNNs, MLPs, and radiomics, achieving a validation accuracy of 76.2%.
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.