Towards Precision in Neuroimaging: A Guiding Framework for Hybrid Neural Architectures Integrating ILSTM-RNN and ERCNN-DRM for Enhanced Brain Tumor Classification
TL;DR
The results obtained in the experiments prove the possibility of the framework in the effective and automatic classification of benign and malignant brain tumours that precondition its use as the promising solution of intelligent computer-assisted healthcare systems.
Abstract
: Early identification and proper classification of brain tumours are essential to making the correct clinical decision. Magnetic Resonance Imaging (MRI) provides high resolution structural data, but the tumor variability in terms of size, shape and texture are a common limiting factor in the performance of traditional Computer-Aided Diagnosis (CAD) systems. In this study, an augmented radial basis function networks (ARBFN), novel long-short-term memory repeat neural networks (ILSTM-RNN), and efficient regularized convolutional neural networks along with dimension reduction modules (ERCNN-DRM) are proposed as a form of deep learning-based CAD framework. MRI images formatted in DICOM are subjected to bilateral filtering, semantic segmentation, and feature reduction through Principal Component Analysis (PCA) to maintain distinctive characteristics. The ERCNN-DRM classifier reached an accuracy of 95.7%, surpassing current techniques in both sensitivity and specificity, while considerably lowering computational demands. The results obtained in the experiments prove the possibility of the framework in the effective and automatic classification of benign and malignant brain tumours that precondition its use as the promising solution of intelligent computer-assisted healthcare systems.