FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification

• URL: http://arxiv.org/abs/2411.02637v1
• Date: Mon, 04 Nov 2024 21:55:52 GMT
• Title: FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification
• Authors: Bidisha Chakraborty, Shree Mitra,
• Abstract summary: 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. We have achieved a validation accuracy of 76.2% in the capsule endoscopy video frame classification task.
• Score: 0.0
• License:
• Abstract: 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.

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