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Breast ultrasound image despeckling using multi-filtering DFrFT and adaptive fast BM3D.
Comput Methods Programs Biomed
April 2024
School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Improving the quality of breast ultrasound images is of great significance for clinical diagnosis which can greatly boost the diagnostic accuracy of ultrasonography. However, due to the influence of ultrasound imaging principles and acquisition equipment, the collected ultrasound images naturally contain a large amount of speckle noise, which leads to a decrease in image quality and affects clinical diagnosis. To overcome this problem, we propose an improved denoising algorithm combining multi-filter DFrFT (Discrete Fractional Fourier Transform) and the adaptive fast BM3D (Block Matching and 3D collaborative filtering) method.
View Article and Find Full Text PDFA Discrete-Mapping-Based Cross-Component Prediction Paradigm for Screen Content Coding.
IEEE Trans Image Process
December 2023
Cross-component prediction is an important intra-prediction tool in the modern video coders. Existing prediction methods to exploit cross-component correlation include cross-component linear model and its extension of multi-model linear model. These models are designed for camera captured content.
View Article and Find Full Text PDFGene selection and tumor identification based on a hybrid of the multi-filter embedded recursive mountain gazelle algorithm.
Comput Biol Med
December 2023
Computer Science Department, Faculty of Computers and Information, Minia University, Minia, Egypt. Electronic address:
Microarray gene expression data are useful for identifying gene expression patterns associated with cancer outcomes; however, their high dimensionality make it difficult to extract meaningful information and accurately classify tumors. Hence, developing effective methods for reducing dimensionality while preserving relevant information is a crucial task. Hybrid-based gene selection methods are widely proposed in the gene expression analysis domain and can still be enhanced in terms of efficiency and reliability.
View Article and Find Full Text PDFColon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms have emerged as a promising approach for non-invasive colon cancer classification using genetic data or patient demographics and medical history.
View Article and Find Full Text PDFAccurate prediction of glioma grades from radiomics using a multi-filter and multi-objective-based method.
Math Biosci Eng
January 2023
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China.
Radiomics, providing quantitative data extracted from medical images, has emerged as a critical role in diagnosis and classification of diseases such as glioma. One main challenge is how to uncover key disease-relevant features from the large amount of extracted quantitative features. Many existing methods suffer from low accuracy or overfitting.
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