Content-based medical image retrieval (CBMIR) is one of the most challenging and ambiguous tasks used to minimize the semantic gap between images and human queries in datasets with rich information content. Similar to the human visual saliency mechanism, CBMIR systems also use the visual features in the images for searching purposes. As a result of this search process, automatically accessing the images is very convenient in large and balanced datasets. Still, it is generally not possible to find such datasets in the medical domain. In this study, a four-step and effective hash code generation technique is presented to reduce the semantic gap between low-level features and high-level semantics for unbalanced medical image datasets. In the first stage, the convolutional neural network (CNN) architecture, the most effective feature representation method available today, is employed to extract discriminative features from images automatically. The features obtained in the last fully connected layer (FCL) at the output of the CNN architecture are used for hash code generation. In the second stage, using the Synthetic Minority Over-sampling Technique (SMOTE), the imbalance between the classes in the dataset is reduced. The solution to the unbalanced problem increases performance by almost 3%. In the third stage, balanced features are converted to a code of 13 symbols by using deep stacked auto-encoder. Finally, this code is translated to the standard 13-character labeling and retrieval code used by the 'Image retrieval in the medical application' (IRMA) dataset, since this is the database with which experiments have been done. IRMA error parameter, classification performance, and retrieval performance of the proposed method are more successful than other state-of-the-art methods.

基于内容的医学图像检索（CBMIR）是最具挑战性和歧义性的任务之一，用于最小化具有丰富信息内容的数据集中图像与人类查询之间的语义鸿沟。类似于人类的视觉显着性机制，CBMIR系统还使用图像中的视觉特征进行搜索。搜索过程的结果是，在大型且平衡的数据集中，自动访问图像非常方便。但是，在医学领域通常还是找不到这种数据集。在这项研究中，提出了一种四步有效的哈希码生成技术，以减少不平衡医学图像数据集的低级特征和高级语义之间的语义鸿沟。在第一阶段，卷积神经网络（CNN）架构 当今可用的最有效的特征表示方法用于自动从图像中提取判别特征。在CNN体系结构的输出中，在最后一个完全连接层（FCL）中获得的功能用于生成哈希码。在第二阶段，使用综合少数族裔过采样技术（SMOTE），减少了数据集中类别之间的不平衡。解决不平衡问题的性能提高了近3％。在第三阶段，通过使用深度堆叠自动编码器将平衡特征转换为13个符号的代码。最后，此代码被转换为“医疗应用中的图像检索”（IRMA）数据集使用的标准13个字符的标签和检索代码，因为这是进行实验的数据库。