An image retrieval system for medical images aids in disease diagnosis by providing similar images from the medical database to a query image. In this article, a content-based medical image retrieval (CBMIR) system is proposed for the retrieval of magnetic resonance imaging (MRI) images of the brain with three types of tumors:- meningioma, glioma and pituitary tumors. The proposed system uses GoogLeNet encodings via transfer learning as image features. A Siamese Neural Network (SNN), is designed, to represent the GoogLeNet encodings in a two-dimensional (2-D) feature space. The SNN is trained using the contrastive loss function to learn the class-specific image features. The similarity, between a query image and the database images, is measured by the Euclidean metric in the lower dimensional feature space. The proposed method achieves state-of-the-art performance for the retrieval of MRI images with brain tumors. The evaluation is done on the openly available Figshare dataset and the performance metrics used are mean average precision (mAP) and precision@10.

用于医学图像的图像检索系统通过提供从医学数据库到查询图像的相似图像来帮助疾病诊断。在本文中，提出了一种基于内容的医学图像检索（CBMIR）系统，用于检索具有三种类型的肿瘤：脑膜瘤，神经胶质瘤和垂体瘤的大脑的磁共振成像（MRI）图像。提出的系统通过传输学习将GoogLeNet编码用作图像特征。设计了一个暹罗神经网络（SNN），以表示二维（2-D）特征空间中的GoogLeNet编码。使用对比损失函数训练SNN，以学习特定于类别的图像特征。查询图像和数据库图像之间的相似性是通过低维特征空间中的欧几里得度量来度量的。所提出的方法实现了具有脑肿瘤的MRI图像检索的最新性能。评估是在公开可用的Figshare数据集上进行的，所使用的性能指标为平均平均精度（mAP）和precision @ 10。