Department of Computer Science & Engineering, Sahyadri College of Engineering & Management, Mangaluru, India.
Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
Sci Rep. 2024 Oct 29;14(1):26023. doi: 10.1038/s41598-024-77243-7.
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(acc_val) of 86.57% on unseen test data, establishing a baseline for performance. To enhance this, PCA was incorporated for dimensionality reduction, which improved the acc_val 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 acc_val of 95.95%. The most substantial improvement was observed when combining SVM with both HOG, LBP, and PCA, achieving an impressive acc_val of 96.03%, along with an F1 score(F1_val) of 96.00%, precision(prec_val) of 96.02%, and recall(rec_val) 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.
在医学影像领域，由于不同肿瘤类型在视觉上具有相似性，因此准确地对脑肿瘤进行分类仍然是一个重大挑战。本研究通过使用支持向量机（SVM）作为核心分类算法，并结合特征提取技术（如方向梯度直方图（HOG）和局部二值模式（LBP））以及降维技术主成分分析（PCA）来解决多类分类的挑战。该研究利用 Kaggle 提供的数据集，该数据集包含 MRI 图像，分为四类，图像来自不同的解剖平面。最初，SVM 模型在未见测试数据上的准确率（acc_val）为 86.57%，为性能建立了基准。为了提高这一点，采用了 PCA 进行降维，这将 acc_val 提高到 94.20%，证明了减少特征维度在减轻过拟合和增强模型泛化方面的有效性。通过结合 SVM 使用特征提取技术（HOG 和 LBP）进一步提高了性能，从而使 acc_val 达到 95.95%。当将 SVM 与 HOG、LBP 和 PCA 结合使用时，观察到最大的改进，达到了令人印象深刻的 acc_val 为 96.03%，同时 F1 分数（F1_val）为 96.00%，精度（prec_val）为 96.02%，召回率（rec_val）为 96.03%。这种方法不仅可以提高分类性能，还可以提高计算效率，是一种用于多类脑肿瘤预测的稳健而有效的方法。
