Feature Fusion Using Deep Learning Algorithms in Image Classification for Security Purposes by Random Weight Network

Automated threat detection in X-ray security imagery is a critical yet challenging task, where conventional deep learning models often struggle with low accuracy and overfitting. This study addresses these limitations by introducing a novel framework based on feature fusion. The proposed method extr...

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Bibliographic Details
Published in:Applied sciences Vol. 15; no. 16; p. 9053
Main Authors: Kiran, Mustafa Servet, Seyfi, Gokhan, Yilmaz, Merve, Esme, Engin, Wang, Xizhao
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2025
Subjects:
Accuracy
Airports
Algorithms
Automation
Classification
Data mining
Data security
Datasets
Deep learning
Detectors
feature fusion
Learning strategies
Machine learning
Medical research
Portable computers
random weight network
Safety and security measures
X-ray security
X-rays
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:Automated threat detection in X-ray security imagery is a critical yet challenging task, where conventional deep learning models often struggle with low accuracy and overfitting. This study addresses these limitations by introducing a novel framework based on feature fusion. The proposed method extracts features from multiple and diverse deep learning architectures and classifies them using a Random Weight Network (RWN), whose hyperparameters are optimized for maximum performance. The results show substantial improvements at each stage: while the best standalone deep learning model achieved a test accuracy of 83.55%, applying the RWN to a single feature set increased accuracy to 94.82%. Notably, the proposed feature fusion framework achieved a state-of-the-art test accuracy of 97.44%. These findings demonstrate that a modular approach combining multi-model feature fusion with an efficient classifier is a highly effective strategy for improving the accuracy and generalization capability of automated threat detection systems.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app15169053