在文献中，利用磁共振成像（MRI）图像检测脑肿瘤和阿尔茨海默病（AD）等神经系统异常是一个重要的研究课题。许多机器学习模型已被用于准确检测脑部异常。本研究解决了在MRI中检测神经系统异常的问题。这个问题的动机在于需要准确和高效的方法来帮助神经学家诊断这些疾病。此外，许多深度学习技术已经应用于MRI，以开发准确的脑部异常检测模型，但这些网络具有高的时间复杂度。因此，我们提出了一种新颖的基于手工特征的学习网络，以降低时间复杂度并获得高分类性能。本研究提出的模型使用了一种名为金字塔和固定尺寸路径（PFP）的新特征生成架构。所提出的PFP结构的主要目标是通过使用具有多层级和本地特征的关键特征提取器来获得高分类性能。此外，PFP特征提取器通过手工提取器生成低级和高级特征。为了获得PFP的高区分特征提取能力，我们使用了直方图梯度（HOG），因此被命名为PFP-HOG。此外，我们还利用迭代Chi2（IChi2）选择临床显著特征。最后，我们使用十折交叉验证的k最近邻（kNN）进行自动分类。我们利用四个MRI神经数据库（AD数据集、脑肿瘤数据集1、脑肿瘤数据集2和合并数据集）开发了我们的模型。在AD数据集、脑肿瘤数据集1、脑肿瘤数据集2和合并脑MRI数据集上，PFP-HOG和基于IChi2的模型分别达到了100%、94.98%、98.19%和97.80%的分类准确率。这些发现不仅提供了利用MRI准确和稳健地分类各种神经系统疾病的方法，而且有潜力帮助神经学家验证手动MRI脑部异常筛查。© 2023. 作者通过独家许可授予医学影像信息学会。

Detecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.© 2023. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.