scholarly journals Montage based 3D medical image retrieval from traumatic brain injury cohort using deep convolutional neural network

2019 ◽  
Author(s):  
Cailey I. Kerley ◽  
Yuankai Huo ◽  
Shikha Chaganti ◽  
Shunxing Bao ◽  
Mayur B. Patel ◽  
...  
Keyword(s):  
Neural Network ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Deep Convolutional Neural Network ◽  
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.

Improved Content Based Image Retrieval Process Based on Deep Convolutional Neural Network and Salp Swarm Algorithm

2021 ◽  
Author(s):  
Gangavarapu Venkata Satya Kumar ◽  
P. G. Krishna Mohan
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Digital Image ◽  
Medical Image ◽  
The Body ◽  
Deep Convolutional Neural Network ◽  
Content Based Image Retrieval ◽  
Average Precision ◽  
Medical Image Retrieval

Digital image and medical image retrieval from several repositories are improving gradually, so the capacity of repositories increases rapidly. The semantic space is the main issue on content-based image retrieval (CBIR), which exists among the semantic level as well as increases the data recognized through human and low level visible data obtained through the image. The CBIR system utilizes the deep convolutional neural network (DCNN), which is trained to medical image characterization and the digital image by salp swarm optimization algorithm (SSA). The average classification accuracy for medical image is 86.805%, a mean average precision is 79%, Average Recall Rate (ARR) is 91.7% and [Formula: see text]-measure is 84.9%, are achieved during retrieval task. For image retrieval, the Average Precision Rate (APR) improved from 39%, 40%, 36% and 42.5% to 86.8% and the ARR enhanced from 39.5%, 40.5%, 35.5% and 42.5% to 86.8%. The [Formula: see text]-measure is improved from 39.5%, 40.5%, 35.5% and 42.5% to 86.8% as different with Local tetra patterns (LTrP), LOOP, local derivative pattern (LDP) and local mean differential excitation pattern (LMDeP) separately on Corel-1K dataset. The presented method is most suitable for multimodal digital images and medical image retrieval for various parts of the body.

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
Sign in / Sign up

Export Citation Format

Share Document