scholarly journals Medical Image Retrieval Based on Convolutional Neural Network and Supervised Hashing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 51877-51885 ◽  
Author(s):  
Yiheng Cai ◽  
Yuanyuan Li ◽  
Changyan Qiu ◽  
Jie Ma ◽  
Xurong Gao
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Medical Image Retrieval

scholarly journals Medical image retrieval using deep convolutional neural network

2017 ◽  
Vol 266 ◽  
pp. 8-20 ◽  
Author(s):  
Adnan Qayyum ◽  
Syed Muhammad Anwar ◽  
Muhammad Awais ◽  
Muhammad Majid
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Deep Convolutional Neural Network ◽  
Medical Image Retrieval

scholarly journals Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shaomin Zhang ◽  
Lijia Zhi ◽  
Tao Zhou
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Deep Convolutional Neural Network ◽  
Medical Image Retrieval ◽  
Mode Decomposition

Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in CBMIR systems. Features play a vital role in the accuracy and speed of the search process. In this paper, we propose a deep convolutional neural network- (CNN-) based framework to learn concise feature vector for medical image retrieval. The medical images are decomposed into five components using empirical mode decomposition (EMD). The deep CNN is trained in a supervised way with multicomponent input, and the learned features are used to retrieve medical images. The IRMA dataset, containing 11,000 X-ray images, 116 classes, is used to validate the proposed method. We achieve a total IRMA error of 43.21 and a mean average precision of 0.86 for retrieval task and IRMA error of 68.48 and F1 measure of 0.66 on classification task, which is the best result compared with existing literature for this dataset.

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