PDF(Pubmed)
Abstract：
Brain tumors, along with other diseases that harm the neurological system, are a significant contributor to global mortality. Early diagnosis plays a crucial role in effectively treating brain tumors. To distinguish individuals with tumors from those without, this study employs a combination of images and data-based features. In the initial phase, the image dataset is enhanced, followed by the application of a UNet transfer-learning-based model to accurately classify patients as either having tumors or being normal. In the second phase, this research utilizes 13 features in conjunction with a voting classifier. The voting classifier incorporates features extracted from deep convolutional layers and combines stochastic gradient descent with logistic regression to achieve better classification results. The reported accuracy score of 0.99 achieved by both proposed models shows its superior performance. Also, comparing results with other supervised learning algorithms and state-of-the-art models validates its performance.
摘要：
脑肿瘤,以及其他伤害神经系统的疾病，是全球死亡率的重要贡献者。早期诊断在有效治疗脑肿瘤中起着至关重要的作用。为了区分有肿瘤的人和没有肿瘤的人，这项研究采用了图像和基于数据的特征的组合。在初始阶段,图像数据集得到增强，然后应用基于UNet迁移学习的模型将患者准确分类为患有肿瘤或正常。在第二阶段,这项研究利用13个特征结合投票分类器。投票分类器融合了从深度卷积层中提取的特征，并将随机梯度下降与逻辑回归相结合，以获得更好的分类结果。两种提出的模型所达到的0.99的准确度分数表明其优越的性能。此外，将结果与其他监督学习算法和最先进的模型进行比较，可以验证其性能。
