Adaptive disease-centric learning algorithms for brain tumor classification using medical image processing
DOI:
https://doi.org/10.65746/jbrha132Keywords:
brain tumor analysis, medical image processing, MRI-based classification, adaptive feature selection, DAHC, intelligent tumor diagnosis, and computational healthcareAbstract
The accurate analysis of brain tumors using medical image processing techniques remains a critical challenge due to high-dimensional imaging features, intra-tumor variability, and complex tissue structures present a magnetic resonance imaging (MRI) data. Conventional image-based classification methods often depend on static feature extraction and fixed learning models, which limit adaptability and predictive robustness in heterogeneous tumor datasets. To address these limitations, this paper proposes an adaptive disease-centric learning framework for brain tumor classification that are integrates between two novel algorithmic techniques. The framework incorporates an Adaptive Disease-Relevant Feature Selection (ADRFS) algorithm, which dynamically evaluates and refines extracted imaging features based on their contribution to tumor discrimination and inter-feature dependency during the learning process. By allowing that the feature spaces to evolve continuously, ADRFS reduces a redundancy while preserving diagnostically significantly to image characteristics. Building upon the optimized feature representation, a Disease-Aware Hybrid Classification (DAHC) algorithm is introduced to classify brain tumors by integrating similarity-based grouping of tumor patterns with adaptive decision modeling, ensuring that tumor-specific characteristics are explicitly embedded into a classification process. The combined application of ADRFS and DAHC enhances interpretability, reduces computational complexity, and improved classification stability for MRI-based tumor analysis. Experimental results demonstrate that the proposed approach achieves superior tumor classification accuracy and efficiency compared to existing image-based learning methods, producing a suitable for intelligent brain tumors diagnosis and clinical decision support systems.
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Copyright (c) 2026 S. Mohan, N. S. Kavitha, A. Vijayalakshmi, R. S. Ramya
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