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

Source: [FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification](<https://www.sota2.com/research/paper/68fa758a1e209131cea6877e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:36:51.485Z

Authors: Bidisha Chakraborty, Shree Mitra

Published: 2024-11-04T21:55:52.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2411.02637>)

[PDF](<https://arxiv.org/pdf/2411.02637.pdf>)