The majority of primary Central Nervous System (CNS) tumors in the brain are among the most aggressive diseases affecting humans. Early detection of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention and treatment, ultimately improving human life expectancy. Magnetic Resonance Imaging (MRI) stands as the most effective technique to detect brain tumors by generating comprehensive brain images through scans. However, human examination can be error-prone and inefficient due to the complexity, size, and location variability of brain tumors. Recently, automated classification techniques using machine learning (ML) methods, such as Convolutional Neural Network (CNN), have demonstrated significantly higher accuracy than manual screening, while maintaining low computational costs. Nonetheless, deep learning-based image classification methods, including CNN, face challenges in estimating class probabilities without proper model calibration. In this paper, we propose a novel brain tumor image classification method, called SIBOW-SVM, which integrates the Bag-of-Features (BoF) model with SIFT feature extraction and weighted Support Vector Machines (wSVMs). This new approach effectively captures hidden image features, enabling the differentiation of various tumor types and accurate label predictions. Additionally, the SIBOW-SVM is able to estimate the probabilities of images belonging to each class, thereby providing high-confidence classification decisions. We have also developed scalable and parallelable algorithms to facilitate the practical implementation of SIBOW-SVM for massive images. As a benchmark, we apply the SIBOW-SVM to a public data set of brain tumor MRI images containing four classes: glioma, meningioma, pituitary, and normal. Our results show that the new method outperforms state-of-the-art methods, including CNN.

翻译：绝大多数原发性中枢神经系统（CNS）脑肿瘤是影响人类的最具侵袭性的疾病之一。早期检测脑肿瘤类型（良性或恶性、胶质瘤或非胶质瘤）对于癌症预防和治疗至关重要，最终可提高人类预期寿命。磁共振成像（MRI）是通过扫描生成全面脑部图像来检测脑肿瘤的最有效技术。然而，由于脑肿瘤的复杂性、大小及位置变异性，人工检查可能容易出错且效率低下。近年来，使用机器学习（ML）方法（如卷积神经网络CNN）的自动化分类技术已被证明比人工筛查具有显著更高的准确性，同时保持较低的计算成本。然而，包括CNN在内的基于深度学习的图像分类方法在缺乏适当模型校准的情况下，难以估计类别概率。在本文中，我们提出了一种名为SIBOW-SVM的新型脑肿瘤图像分类方法，该方法将词袋模型（BoF）与SIFT特征提取和加权支持向量机（wSVMs）相结合。这种新方法能够有效捕获隐藏的图像特征，从而区分不同类型的肿瘤并准确预测标签。此外，SIBOW-SVM能够估计图像属于每个类别的概率，从而提供高置信度的分类决策。我们还开发了可扩展且可并行的算法，以促进SIBOW-SVM在大量图像中的实际应用。作为基准测试，我们将SIBOW-SVM应用于一个包含四类（胶质瘤、脑膜瘤、垂体瘤和正常组织）的脑肿瘤MRI图像公开数据集。结果表明，新方法优于包括CNN在内的最先进方法。