| Abstrakt: |
The increasing size of medical image repositories is due to the increasing number of digital imaging data sources. Most of the image content descriptors proposed in the literature are not suitable for the retrieval of large medical image datasets. The ability to extract features from an image is a vital criterion that should be considered to evaluate retrieval efficacy. This paper proposes an efficient image retrieval system for medical applications based on the new Canny steerable texture filter (CSTF) feature descriptor and Brownian motion weighting deep learning neural network (BMWDLNN) classifier. Initially, Modified Kuan Filter (MKF) is used to condense the noise in images. Then, the image contrast is enhanced using the Gaussian Linear Contrast Stretching Model (GLCSM) method. Then, the image features are extracted using the CSTF method. Later, the dimensionality of the extracted features is reduced by means of the Mean Correlation Coefficient Component Analysis (MCCCA) method and then the BMWDLNN classifier is applied. For the classified images, the score values are calculated using the Harmonic Mean-based Fisher Score (HMFS) method. Thereafter, various distance values are calculated for the score value of the image and are summed up to find the average. The retrieval outcome is determined by the minimum distance between database images and the query image. The proposed method obtained an average precision rate of 0.9981, 0.9992, 0.9951, and 0.9940 for EXACT-09, TCIA, NEMA-CT, and OASIS databases, respectively. The experimental results revealed that the proposed methodology outperforms the existing methods. [ABSTRACT FROM AUTHOR] |
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