Enhanced Brain Tumour Detection Using Adaptive Preprocessing and Hybrid Deep Learning Techniques

Authors

https://doi.org/10.48314/ceti.vi.77

Abstract

Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool for neurological and brain cancers, which are major global health issues. However, hand-analysed MRI scans are inconsistent and time-consuming. This paper describes a sophisticated strategy for employing Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to detect brain cancers. By combining hybrid Deep Learning (DL) algorithms with superior preprocessing, the framework improves performance. Initially, Adaptive Multi-scale Gaussian Filtering (AMGF) is applied to minimize noise and improve image quality. Following the identification of the most relevant features using Recursive Feature Elimination with Cross-Validation (RFECV), Lévy Flight Particle Swarm Optimization (LFPSO) is used to optimize feature extraction. To perform classification, a hybrid DL model combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, identifying both temporal and spatial patterns accurately. This technique provides significant increases in detection accuracy and processing performance. indicating its potential for clinical use and better patient outcomes. The results shown that the proposed method has the best results around the comparison methods.

Keywords:

Magnetic resonance imaging, Brain tumor detection, Adaptive preprocessing, Hybrid deep learning, Dynamic contrast-enhanced magnetic resonance imaging, Convolutional neural network, Long short-term memory

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Published

2025-07-29

Issue

Vol. 2 No. 3 (2025): Computational Engineering and Technology Innovations

Section

Articles

How to Cite

Pourqasem, J., & Parandavar, Z. (2025). Enhanced Brain Tumour Detection Using Adaptive Preprocessing and Hybrid Deep Learning Techniques. Computational Engineering and Technology Innovations, 2(3), 11-28. https://doi.org/10.48314/ceti.vi.77

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