Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques

In the field of medical imaging, accurately classifying brain tumors remains a significant challenge because of the visual similarities among different tumor types. This research addresses the challenge of multiclass categorization by employing Support Vector Machine (SVM) as the core classification algorithm and analyzing its performance in conjunction with feature extraction techniques such as Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP), as well as the dimensionality reduction technique, Principal Component Analysis (PCA). The study utilizes a dataset sourced from Kaggle, comprising MRI images classified into four classes, with images captured from various anatomical planes. Initially, the SVM model alone attained an accuracy (accᵥal) of 86. 57% on unseen test data, establishing a baseline for performance. To enhance this, PCA was incorporated for dimensionality reduction, which improved the accᵥal to 94. 20%, demonstrating the effectiveness of reducing feature dimensionality in mitigating overfitting and enhancing model generalization. Further performance gains were realized by applying feature extraction techniques-HOG and LBP-in conjunction with SVM, resulting in an accᵥal of 95. 95%. The most substantial improvement was observed when combining SVM with both HOG, LBP, and PCA, achieving an impressive accᵥal of 96. 03%, along with an F1 score (F1ᵥal) of 96. 00%, precision (precᵥal) of 96. 02%, and recall (recᵥal) of 96. 03%. This approach will not only improves categorization performance but also improves efficacy of computation, making it a robust and effective method for multiclass brain tumor prediction.