With a widespread use of digital imaging data in hospitals, the size of
medical image repositories is increasing rapidly. This causes difficulty in
managing and querying these large databases leading to the need of content
based medical image retrieval (CBMIR) systems. A major challenge in CBMIR
systems is the semantic gap that exists between the low level visual
information captured by imaging devices and high level semantic information
perceived by human. The efficacy of such systems is more crucial in terms of
feature representations that can characterize the high-level information
completely. In this paper, we propose a framework of deep learning for CBMIR
system by using deep Convolutional Neural Network (CNN) that is trained for
classification of medical images. An intermodal dataset that contains twenty
four classes and five modalities is used to train the network. The learned
features and the classification results are used to retrieve medical images.
For retrieval, best results are achieved when class based predictions are used.
An average classification accuracy of 99.77% and a mean average precision of
0.69 is achieved for retrieval task. The proposed method is best suited to
retrieve multimodal medical images for different body organs.

提出了一种利用深度卷积神经网络的 CBMIR 系统，该系统在医疗图像分类任务中取得了平均 99.77％的分类准确度和 0.69 的平均平均精度，是用于检索多模态医学图像的最佳方法。