Kaplan Ela, Baygin Mehmet, Barua Prabal D, Dogan Sengul, Tuncer Turker, Altunisik Erman, Palmer Elizabeth Emma, Acharya U Rajendra
Department of Radiology, Adiyaman Training and Research Hospital, Adiyaman, Turkey.
Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey.
Med Eng Phys. 2023 May;115:103971. doi: 10.1016/j.medengphy.2023.103971. Epub 2023 Mar 21.
The classification of medical images is an important priority for clinical research and helps to improve the diagnosis of various disorders. This work aims to classify the neuroradiological features of patients with Alzheimer's disease (AD) using an automatic hand-modeled method with high accuracy.
This work uses two (private and public) datasets. The private dataset consists of 3807 magnetic resonance imaging (MRI) and computer tomography (CT) images belonging to two (normal and AD) classes. The second public (Kaggle AD) dataset contains 6400 MR images. The presented classification model comprises three fundamental phases: feature extraction using an exemplar hybrid feature extractor, neighborhood component analysis-based feature selection, and classification utilizing eight different classifiers. The novelty of this model is feature extraction. Vision transformers inspire this phase, and hence 16 exemplars are generated. Histogram-oriented gradients (HOG), local binary pattern (LBP) and local phase quantization (LPQ) feature extraction functions have been applied to each exemplar/patch and raw brain image. Finally, the created features are merged, and the best features are selected using neighborhood component analysis (NCA). These features are fed to eight classifiers to obtain highest classification performance using our proposed method. The presented image classification model uses exemplar histogram-based features; hence, it is called ExHiF.
We have developed the ExHiF model with a ten-fold cross-validation strategy using two (private and public) datasets with shallow classifiers. We have obtained 100% classification accuracy using cubic support vector machine (CSVM) and fine k nearest neighbor (FkNN) classifiers for both datasets.
Our developed model is ready to be validated with more datasets and has the potential to be employed in mental hospitals to assist neurologists in confirming their manual screening of AD using MRI/CT images.
医学图像分类是临床研究的重要优先事项，有助于改善各种疾病的诊断。本研究旨在使用高精度的自动手工建模方法对阿尔茨海默病（AD）患者的神经放射学特征进行分类。
本研究使用了两个（私人和公共）数据集。私人数据集由属于两个类别（正常和AD）的3807张磁共振成像（MRI）和计算机断层扫描（CT）图像组成。第二个公共（Kaggle AD）数据集包含6400张MR图像。所提出的分类模型包括三个基本阶段：使用示例混合特征提取器进行特征提取、基于邻域成分分析的特征选择以及使用八个不同分类器进行分类。该模型的新颖之处在于特征提取。视觉Transformer启发了这一阶段，因此生成了16个示例。已将面向直方图的梯度（HOG）、局部二值模式（LBP）和局部相位量化（LPQ）特征提取函数应用于每个示例/补丁和原始脑图像。最后，将创建的特征合并，并使用邻域成分分析（NCA）选择最佳特征。这些特征被输入到八个分类器中，以使用我们提出的方法获得最高的分类性能。所提出的图像分类模型使用基于示例直方图的特征；因此，它被称为ExHiF。
我们使用两个（私人和公共）数据集，通过十折交叉验证策略，使用浅层分类器开发了ExHiF模型。对于这两个数据集，我们使用立方支持向量机（CSVM）和精细k近邻（FkNN）分类器获得了100%的分类准确率。
我们开发的模型准备好用更多数据集进行验证，并且有潜力在精神病院用于协助神经科医生使用MRI/CT图像确认他们对AD的手动筛查。
