In medical applications, retrieving similar images from repositories is most essential for supporting diagnostic imaging-based clinical analysis and decision support systems. However, this is a challenging task, due to the multi-modal and multi-dimensional nature of medical images. In practical scenarios, the availability of large and balanced datasets that can be used for developing intelligent systems for efficient medical image management is quite limited. Traditional models often fail to capture the latent characteristics of images and have achieved limited accuracy when applied to medical images. For addressing these issues, a deep neural network-based approach for view classification and content-based image retrieval is proposed and its application for efficient medical image retrieval is demonstrated. We also designed an approach for body part orientation view classification labels, intending to reduce the variance that occurs in different types of scans. The learned features are used first to predict class labels and later used to model the feature space for similarity computation for the retrieval task. The outcome of this approach is measured in terms of error score. When benchmarked against 12 state-of-the-art works, the model achieved the lowest error score of 132.45, with 9.62–63.14% improvement over other works, thus highlighting its suitability for real-world applications.

中文翻译：

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基于多视图分类的基于内容的医学图像检索的深度神经网络模型

在医学应用中，从存储库中检索相似图像对于支持基于诊断成像的临床分析和决策支持系统至关重要。然而，由于医学图像的多模态和多维性质，这是一项具有挑战性的任务。在实际场景中，可用于开发智能系统以实现高效医学图像管理的大型平衡数据集的可用性非常有限。传统模型通常无法捕捉图像的潜在特征，并且在应用于医学图像时精度有限。为了解决这些问题，提出了一种基于深度神经网络的视图分类和基于内容的图像检索方法，并展示了其在高效医学图像检索中的应用。我们还设计了一种身体部位方向视图分类标签的方法，旨在减少不同类型扫描中出现的差异。学习到的特征首先用于预测类标签，然后用于为检索任务的相似度计算建模特征空间。这种方法的结果是根据错误评分来衡量的。当对 12 个最先进的作品进行基准测试时，该模型的错误得分最低，为 132.45，比其他作品提高了 9.62-63.14%，从而突出了它对现实世界应用的适用性。这种方法的结果是根据错误评分来衡量的。当对 12 个最先进的作品进行基准测试时，该模型的错误得分最低，为 132.45，比其他作品提高了 9.62-63.14%，从而突出了它对现实世界应用的适用性。这种方法的结果是根据错误评分来衡量的。当对 12 个最先进的作品进行基准测试时，该模型的错误得分最低，为 132.45，比其他作品提高了 9.62-63.14%，从而突出了它对现实世界应用的适用性。