Robust Brain Tumor MRI Classification Through MobileNetV3 Deep Feature Fusion and Principal Component Analysis Enhanced AdaBoost Learning - Ahmed Aizaldeen Abdullah; Hadeel Safaa Hussein; Laith Ali Abdul Rahaim (Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2026)
Among the most serious neurological diseases are brain tumors, which pose a challenge to early detection through MRI due to low contrast, tissue heterogeneity, and high-dimensional deep features that make it difficult for traditional classification models to be effective. This study proposes a robust and computationally efficient multi-class classification framework capable of distinguishing four tumor types: glioma, meningioma, pituitary tumor, and no tumor. The primary contributions are: (1) the development of a hybrid feature-learning pipeline that introduces a hybrid feature-learning framework in which a one-level 2D Discrete Wavelet Transform (2D-DWT) is employed as a multi-resolution preprocessing step to enhance MRI slices prior to deep feature extraction using MobileNetV3; (2) the application of Principal Component Analysis (PCA) to compress a 1,024-dimensional deep-feature vector into only 20 principal components, achieving a 99.96% reduction in dimensionality; (3) the use of an optimized AdaBoost ensemble specifically adapted for low-dimensional inputs; and (4) achieving performance that surpasses several published approaches evaluated on the same benchmark dataset. The proposed workflow includes cropping, normalization, and CLAHE enhancement, followed by 2D-DWT to extract LL, LH, HL, and HH sub-band information. The wavelet-refined MRI slices are processed by MobileNetV3 to implicitly encode spectral–textural information into deep semantic representations, which are subsequently reduced using PCA and classified by AdaBoost. Experiments conducted on a public Kaggle brain MRI dataset comprising 7023 images show that MobileNetV3 combined with 2D-DWT achieves an accuracy of 99.56%. When enhanced with PCA and AdaBoost, the full framework attains 99.94% accuracy, 99.95% precision, 99.96% recall, 99.94% F1-score, and 100% AUC, demonstrating remarkable tumor discrimination performance. In summary, the proposed PCA–AdaBoost hybrid framework offers a highly accurate, lightweight, and clinically promising solution for automated brain tumor MRI classification.