A DEEP LEARNING ENSEMBLE APPROACH FOR X-RAY IMAGE CLASSIFICATION

Year 2024, , 700 - 713, 01.09.2024

Engin Eşme , Mustafa Servet Kıran

https://doi.org/10.36306/konjes.1424329

Abstract

The application of deep learning-based intelligent systems for X-ray imaging in various settings, including transportation, customs inspections, and public security, to identify hidden or prohibited objects are discussed in this study. In busy environments, x-ray inspections face challenges due to time limitations and a lack of qualified personnel. Deep learning algorithms can automate the imaging process, enhancing object detection and improving safety. This study uses a dataset of 5094 x-ray images of laptops with hidden foreign circuits and normal ones, training 11 deep learning algorithms with the 10-fold cross-validation method. The predictions of deep learning models selected based on the 70% threshold value have been combined using a meta-learner. ShuffleNet has the highest individual performance with 83.56%, followed by InceptionV3 at 81.30%, Darknet19 at 78.92%, DenseNet201 at 77.70% and Xception at 71.26%. Combining these models into an ensemble achieved a remarkable classification success rate of 85.97%, exceeding the performance of any individual model. The ensemble learning approach provides a more stable prediction output, reducing standard deviation among folds as well. This research highlights the potential for safer and more effective X-ray inspections through advanced machine learning techniques.

Keywords

Deep Learning, Ensemble Learning, Object Classification, X-Ray

Supporting Institution

The Scientific and Technological Research Council of Türkiye

Project Number

122E024

Thanks

This study has been supported by The Scientific and Technological Research Council of Türkiye under Grant 122E024. The authors thank the council for the institutional supports.

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