Combined Filtering Approach Research Articles

Early Breast Cancer Detection Using Artificial Intelligence Techniques Based on Advanced Image Processing Tools

The early detection of breast cancer is essential for improving treatment outcomes, and recent advancements in artificial intelligence (AI), combined with image processing techniques, have shown great potential in enhancing diagnostic accuracy. This study explores the effects of various image processing methods and AI models on the performance of early breast cancer diagnostic systems. By focusing on techniques such as Wiener filtering and total variation filtering, we aim to improve image quality and diagnostic precision. The novelty of this study lies in the comprehensive evaluation of these techniques across multiple medical imaging datasets, including a DCE-MRI dataset for breast-tumor image segmentation and classification (BreastDM) and the Breast Ultrasound Image (BUSI), Mammographic Image Analysis Society (MIAS), Breast Cancer Histopathological Image (BreakHis), and Digital Database for Screening Mammography (DDSM) datasets. The integration of advanced AI models, such as the vision transformer (ViT) and the U-KAN model—a U-Net structure combined with Kolmogorov–Arnold Networks (KANs)—is another key aspect, offering new insights into the efficacy of these approaches in different imaging contexts. Experiments revealed that Wiener filtering significantly improved image quality, achieving a peak signal-to-noise ratio (PSNR) of 23.06 dB and a structural similarity index measure (SSIM) of 0.79 using the BreastDM dataset and a PSNR of 20.09 dB with an SSIM of 0.35 using the BUSI dataset. When combined filtering techniques were applied, the results varied, with the MIAS dataset showing a decrease in SSIM and an increase in the mean squared error (MSE), while the BUSI dataset exhibited enhanced perceptual quality and structural preservation. The vision transformer (ViT) framework excelled in processing complex image data, particularly with the BreastDM and BUSI datasets. Notably, the Wiener filter using the BreastDM dataset resulted in an accuracy of 96.9% and a recall of 96.7%, while the combined filtering approach further enhanced these metrics to 99.3% accuracy and 98.3% recall. In the BUSI dataset, the Wiener filter achieved an accuracy of 98.0% and a specificity of 98.5%. Additionally, the U-KAN model demonstrated superior performance in breast cancer lesion segmentation, outperforming traditional models like U-Net and U-Net++ across datasets, with an accuracy of 93.3% and a sensitivity of 97.4% in the BUSI dataset. These findings highlight the importance of dataset-specific preprocessing techniques and the potential of advanced AI models like ViT and U-KAN to significantly improve the accuracy of early breast cancer diagnostics.

A Combined Filtering Approach to High-Frequency Volatility Estimation with Mixed-Type Microstructure Noises

A Combined Filtering Approach to High-Frequency Volatility Estimation with Mixed-Type Microstructure Noises

Sex differences in kidney gene expression during the life cycle of F344 rats

BackgroundThe kidney functions in key physiological processes to filter blood and regulate blood pressure via key molecular transporters and ion channels. Sex-specific differences have been observed in renal disease incidence and progression, as well as acute kidney injury in response to certain drugs. Although advances have been made in characterizing the molecular components involved in various kidney functions, the molecular mechanisms responsible for sex differences are not well understood. We hypothesized that the basal expression levels of genes involved in various kidney functions throughout the life cycle will influence sex-specific susceptibilities to adverse renal events.MethodsWhole genome microarray gene expression analysis was performed on kidney samples collected from untreated male and female Fischer 344 (F344) rats at eight age groups between 2 and 104 weeks of age.ResultsA combined filtering approach using statistical (ANOVA or pairwise t test, FDR 0.05) and fold-change criteria (>1.5 relative fold change) was used to identify 7,447 unique differentially expressed genes (DEGs). Principal component analysis (PCA) of the 7,447 DEGs revealed sex-related differences in mRNA expression at early (2 weeks), middle (8, 15, and 21 weeks), and late (104 weeks) ages in the rat life cycle. Functional analysis (Ingenuity Pathway Analysis) of these sex-different genes indicated over-representation of specific pathways and networks including renal tubule injury, drug metabolism, and immune cell and inflammatory responses. The mRNAs that code for the qualified urinary protein kidney biomarkers KIM-1, Clu, Tff3, and Lcn2 were also observed to show sex differences.ConclusionsThese data represent one of the most comprehensive in-life time course studies to be published, assessing sex differences in global gene expression in the F344 rat kidney. PCA and Venn analyses reveal specific periods of sexually dimorphic gene expression which are associated with functional categories (xenobiotic metabolism and immune cell and inflammatory responses) of key relevance to acute kidney injury and chronic kidney disease, which may underlie sex-specific susceptibility. Analysis of the basal gene expression patterns of renal genes throughout the life cycle of the rat will improve the use of current and future renal biomarkers and inform our assessments of kidney injury and disease.