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<p>The efficient and precise classification of brain tumors is important for early identification and appropriate action. This study presents a novel technique called Probabilistic Bi-Level Teaching–Learning-Based Optimization (P-BTLBO) for hybrid feature extraction and multi-class brain tumor classification. The P-BTLBO method combines probabilistic modeling with a bi-level optimization framework to make feature selection better. This makes it easier to explore and exploit in environments with a lot of dimensions. Deep features from ResNet-50, a convolutional neural network, are combined with texture-based features from gray level co-occurrence matrix (GLCM) analysis in the hybrid feature extraction method. These complementary attributes capture both predominant patterns and detailed texture information from magnetic resonance imaging (MRI) scans, facilitating thorough tumor characterization. The suggested P-BTLBO method makes the combined set of features better by repeatedly focusing on higher-level and lower-level goals: improving classification accuracy while removing unnecessary features. To assess the efficacy of the optimized features, various classifiers, such as support vector machine (SVM), k-nearest neighbors (k-NN), and decision tree, were examined. Experimental findings indicate that the P-BTLBO algorithm surpasses conventional optimization methods, including TLBO and PSO, regarding classification accuracy, feature subset size, and computational efficiency. The hybrid framework attains enhanced multi-class categorization of glioma, meningioma, pituitary tumors, and healthy cases, presenting a valuable instrument for clinical diagnosis. This research underscores the efficacy of P-BTLBO in tackling hierarchical optimization issues in medical imaging and outlines its applicability in other intricate fields.</p>

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A novel Probabilistic Bi-Level Teaching–Learning-Based Optimization (P-BTLBO) algorithm for hybrid feature extraction and multi-class brain tumor classification using ResNet-50 and GLCM

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月7日

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  • Mahananda Malkauthekar,
  • Avinash Gulve,
  • Ratnadeep Deshmukh

摘要

The efficient and precise classification of brain tumors is important for early identification and appropriate action. This study presents a novel technique called Probabilistic Bi-Level Teaching–Learning-Based Optimization (P-BTLBO) for hybrid feature extraction and multi-class brain tumor classification. The P-BTLBO method combines probabilistic modeling with a bi-level optimization framework to make feature selection better. This makes it easier to explore and exploit in environments with a lot of dimensions. Deep features from ResNet-50, a convolutional neural network, are combined with texture-based features from gray level co-occurrence matrix (GLCM) analysis in the hybrid feature extraction method. These complementary attributes capture both predominant patterns and detailed texture information from magnetic resonance imaging (MRI) scans, facilitating thorough tumor characterization. The suggested P-BTLBO method makes the combined set of features better by repeatedly focusing on higher-level and lower-level goals: improving classification accuracy while removing unnecessary features. To assess the efficacy of the optimized features, various classifiers, such as support vector machine (SVM), k-nearest neighbors (k-NN), and decision tree, were examined. Experimental findings indicate that the P-BTLBO algorithm surpasses conventional optimization methods, including TLBO and PSO, regarding classification accuracy, feature subset size, and computational efficiency. The hybrid framework attains enhanced multi-class categorization of glioma, meningioma, pituitary tumors, and healthy cases, presenting a valuable instrument for clinical diagnosis. This research underscores the efficacy of P-BTLBO in tackling hierarchical optimization issues in medical imaging and outlines its applicability in other intricate fields.