TRAITEMENT DU SIGNAL, vol.41, no.2, pp.1-10, 2024 (SCI-Expanded)
For detecting and classifying brain tumors,
clinicians use Magnetic Resonance Imaging (MRI) data. Automated AI-powered
tools accelerate the diagnostic process for clinicians. However, large amounts
of data are needed for these models to achieve high accuracy. Variational
Autoencoders (VAE) and Generative Adversarial Networks (GAN) architecture are
combined for dataset expansion. The accuracy was improved with the artificial
image set created in all tested models. However, since the accuracy rate
remained at 92,960% using Long Short Term Memory Algorithm, it was observed
that a hybrid method was also needed, and hybrid Elmann Bidirectional Long
Short Memory Algorithm (Elmann-BiLSTM) was developed. In this proposed approach
based on deep learning, a Guided Bilateral Filter is used to separate skull
from images after VAE-GAN structure. The thresholding scheme extracts tumour
regions from the original image in parts. Edge features and major texture data
are collected from these tumor images produced using the Improved Gabor Wavelet
Transform. Random Forest-based feature selection algorithm will select optimal
features that increase accuracy from extracted features. These features feed
the Elmann-BiLSTM algorithm used as a two-step classifier. The accuracy rate
was 98.897% in the one-step classification approach and 100% and 99.313% in the
two-step classifier approach, respectively.