Improving the ranking quality of medical image retrieval using a genetic feature selection method

2011 ◽  
Vol 51 (4) ◽  
pp. 810-820 ◽  
Author(s):  
Sérgio Francisco da Silva ◽  
Marcela Xavier Ribeiro ◽  
João do E.S. Batista Neto ◽  
Caetano Traina-Jr. ◽  
Agma J.M. Traina
Keyword(s):  
Feature Selection ◽  
Image Retrieval ◽  
Medical Image ◽  
Feature Selection Method ◽  
Selection Method ◽  
Medical Image Retrieval ◽  
Genetic Feature ◽  
Genetic Feature Selection

scholarly journals An Efficient Medical Image Retrieval and Classification using Deep Neural Network

2020 ◽  
Vol 13 (39) ◽  
pp. 4127-4141
Author(s):  
K Chethan
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Image Retrieval ◽  
Medical Image ◽  
Deep Neural Network ◽  
Retrieval Performance ◽  
Medical Image Retrieval ◽  
Occurrence Pattern ◽  
Sensitivity Specificity ◽  
Nonlinear Feature

Background/Objectives: The main objective of this work is to obtain an efficient brain tumor image retrieval and classification using Deep Neural Network (DNN). Methods/Statistical analysis: The features from the medical images are extracted by using tamura feature extraction, Local Ternary Pattern (LTP) and Histogram of Oriented Gradients (HOG). Subsequently, an Infinite Feature Selection (Inf-FS) technique is incorporated to select optimum features from feature vector, which leads to improve the classification process using sparse auto encoder based DNN. Furthermore, the retrieval performance of the proposed method is improved by Euclidean Distance technique. Findings: An Open Access Series of Imaging Studies (OASIS) and Contrast Enhanced- Magnetic Resonance Image (CE-MRI) datasets are utilized to analyze the proposed method. The sparse auto encoder based DNN classification scheme yields an overall accuracy of 95.34% in OASIS dataset and 99.87% in CEMRI dataset with improved sensitivity, specificity, error rate. The retrieval performance of proposed technique is assessed in terms of Average Retrieval Precision (ARP) and compared with two existing methods such as Local Mesh Vector Co-occurrence Pattern (LMVCoP) and Content Based Image Retrieval- Convolutional Neural Network (CBIR-CNN). The ARP of the proposed method for CE-MRI and OASIS dataset is 98.33% and 88.25% that is high when compared to the CBIR-CNN, LMVCoP method. Novelty/Applications: An appropriate feature selection using Inf-FS and DNN based nonlinear feature data classification are used in the applications of medical image retrieval.

Improvement of feature selection method in spam filtering

2009 ◽  
Vol 29 (10) ◽  
pp. 2812-2815
Author(s):  
Yang-zhu LU ◽  
Xin-you ZHANG ◽  
Yu QI
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Spam Filtering
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