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Research Article |

Multi-Model Deep Feature Fusion for Robust Detection of Neuro-Oncological Abnormalities

Author(s): Yasser Nizamli1*, Anton Filatov2, Weaam Fadel3, Yulia Shichkina3, Kinda Mreish4,5, Nahida Karaja1, and Tarek Alnajar6

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 4

Publisher : FOREX Publication

Published : 15 December 2025

e-ISSN : 2347-470X

Page(s) : 802-812

DOI: https://doi.org/10.37391/IJEER.130420

Abstract

Accurate and early diagnosis of brain tumors can significantly reduce both the invasiveness and cost of therapeutic interventions while preserving neurological function. Current limitations in manual MRI analysis, including human error, variability in expertise, and interpretive inconsistencies, have created a pressing need for advanced diagnostic systems based on artificial intelligence and deep learning techniques. This study presents a hybrid transfer learning approach designed to enhance the detection of neuro-oncological abnormalities. Our methodology employs parallel processing of MRI images through pre-trained DenseNet121 and VGG16 architectures to extract discriminative numerical features. These feature sets are integrated and then processed through principal component analysis to improve computational efficiency. For the classification stage, we implement both support vector machine and k-nearest neighbors algorithms independently. A comprehensive evaluation, including an analysis of data processing sequences to ensure methodological rigor, demonstrates that the proposed feature fusion framework achieves robust performance and competitive accuracy, exceeding the performance of several contemporary deep learning models in this domain.

Keywords: Anomaly detection, Brain tumors, Medical Imaging, MRI Images, Deep learning, Feature fusion.




Yasser Nizamli*, Department of Software Engineering and Computer Applications, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia; Department of Computer Engineering and Automatic Control, Latakia University, Latakia, Syria; Email: yanizamli@stud.etu.ru

Anton Filatov, Department of Software Engineering and Computer Applications, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia

Weaam Fadel, Department of Computer Engineering, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia

Yulia Shichkina, Department of Computer Engineering, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia

Kinda Mreish, Department of Computer Engineering, Aleppo University, Aleppo, Syria; Department of Information Systems, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia

Nahida Karaja, Department of Software Engineering and Computer Applications, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia

Tarek Alnajar, Department of Automatic Control Systems, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia

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Yasser Nizamli, Anton Filatov, Weaam Fadel, Yulia Shichkina, Kinda Mreish, Nahida Karaja, Tarek Alnajar (2025), Multi-Model Deep Feature Fusion for Robust Detection of Neuro-Oncological Abnormalities. IJEER 13(4), 802-812. DOI: 10.37391/IJEER.130420.