Developing automated Computer Aided Diagnosis (CAD) framework for assisting radiologists in a fast and effective classification of brain Magnetic Resonance (MR) images is of great importance, given plausible usage of Electronic Health Records (EHR) in healthcare system. This work aims at proposing two novel privacy supporting classifiers for automatic segregation of brain MR images. To ensure privacy, our article employs a spatial steganographic approach to hide patients sensitive health information in brain images itself. Proposed methods employ Discrete Wavelet Transform (DWT) for extracting relevant features from original and stego images. Subsequently, Symmetrical Uncertainty Ranking (SUR) and Probabilistic Principal Components Analysis (PPCA) are used to obtain a reduced feature vector for Support Vector Machine (SVM) and Filtered Classifier (FC) respectively. The experiments are carried out on two benchmark datasets DS-75 and DS-160 collected from Harvard Medical School website and one larger input pool of self-collected dataset NITR-DHH. To validate this work, the proposed schemes are experimented on both original and stego brain MR images and are compared against eight state-of-the-art classification techniques with respect to six standard parameters. The results reveal that the proposed techniques are robust and scalable with respect to the size of the datasets. Moreover, it is concluded that applying steganographic algorithm on brain MR images yield equally satisfactory classification rate.

鉴于在医疗保健系统中合理使用电子病历（EHR），开发自动计算机辅助诊断（CAD）框架以帮助放射科医生快速有效地对脑部磁共振（MR）图像进行分类非常重要。这项工作旨在提出两个新颖的隐私支持分类器，用于脑MR图像的自动隔离。为了确保隐私，我们的文章采用空间隐写方法将患者敏感的健康信息隐藏在大脑图像本身中。提出的方法采用离散小波变换（DWT）从原始图像和隐秘图像中提取相关特征。后来，对称不确定性排名（SUR）和概率主成分分析（PPCA）用于分别获得支持向量机（SVM）和滤波分类器（FC）的简化特征向量。实验是从哈佛医学院网站收集的两个基准数据集DS-75和DS-160以及一个更大的自收集数据集NITR-DHH的输入库中进行的。为了验证这项工作，在原始图像和隐身大脑MR图像上都对提出的方案进行了实验，并针对六个标准参数与八种最新的分类技术进行了比较。结果表明，相对于数据集的大小，所提出的技术是可靠且可扩展的。此外，