Work

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

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2411.02637

Authors: Chakraborty, Bidisha, Mitra, Shree

Institutions: Government College of Engineering and Leather Technology, Indian Institute of Information Technology Guwahati

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green

OpenAlex ID: W4404356289

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