ArticleScientific reports2025
A novel brain MRI classification framework integrating tuned single scale retinex and empirical wavelet entropy features.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Toward reliable computer-aided brain tumor diagnosis: a contrast-enhanced deep learning approach with hybrid KNN classification.Frontiers in human neuroscience · 2026Article
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10 authors.
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Abstract
Brain tumors are caused by the abnormal growth of cells in the brain, which are often irregular in shape. Growing at such a rate of about 1.4% per day, the abnormal rate of growth accounts for an invisible illness and depressive behavioral changes; hence, brain tumors have become a major cause of increased death rates among adults worldwide. The prognosis of a brain tumor can be improved significantly if the tumor is diagnosed early using different imaging modalities. Out of these various imaging systems, Magnetic Resonance Imaging (MRI) is the most commonly used diagnostic modality because it is non-invasive and provides clear visualization of brain tissues. This study proposes a novel brain MRI image classification framework involving image enhancement, adaptive decomposition, statistical feature extraction, and a supervised classification method. First, the contrast of magnetic resonance is improved through the Tuned Single-Scale Retinex (TSSR) technique. Afterward, these enhanced images are decomposed through Empirical Wavelet Transform (EWT) so that informative subbands can be extracted. From each subband, energy and entropy features (Shannon and Tsallis) are computed and concatenated into a feature vector. This feature set is used to train Support Vector Machine (SVM) and LPBoost classifiers. The model was evaluated on a binary-class brain MRI dataset comprising 280 images sourced from the Harvard Medical School and Kaggle repositories. Experimental results demonstrate that the proposed framework achieves a classification accuracy of 96.43%, with a true positive rate (TPR) of 100% and a true negative rate (TNR) of 77.78%, outperforming several state-of-the-art methods. The primary contributions include the introduction of EWT-based statistical features for brain MRI classification and the use of TSSR for enhanced image quality, offering a robust and generalizable solution for medical image analysis.
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