Medical image retrieval using deep convolutional neural network

By Adnan Qayyum, Syed Muhammad Anwar, Muhammad Awais, Muhammad Majid. 2017

400 citations recorded by the source corpus.

Abstract 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “Medical CBIR is now, however, dominated by deep learning algorithms [311, 312, 313].”
A Decade Survey of Content Based Image Retrieval Using Deep Learning (2020, methodology): “retrieval pertaining to different applications such as cloth retrieval [189], biomedical image retrieval [190], [191], face retrieval [230], [231], remote sensing image retrieval [142], landmark retrieval”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, methodology): “sufficient and various ML/DL techniques are proposed in the literature [60], [61].”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, methodology): “…based methods have provided state-of-the-art performance for various healthcare tasks including medical image reconstruction [1], management of electronic health records [2], cancer segmentation [3], disease prediction [4], clinical imaging [5], image retrieval [6], and computational biology [7].”
Application of the residue number system to reduce hardware costs of the convolutional neural network implementation (2020, background): “…of tasks requiring the use of artificial intelligence methods in the field of image processing is constantly expanding: personal identification [5], scene recognition [6], information processing from external sensors in unmanned land and aircraft vehicles [28], medical diagnostics [23], and so on.”