MSE, Mean Square Error
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Chronic hyperplastic candidiasis (CHC) presents a distinctive and relatively rare form of oral candidal infection characterized by the presence of white or white-red patches on the oral mucosa. Often mistaken for leukoplakia or erythroleukoplakia due to their appearance, these lesions display nonhomogeneous textures featuring combinations of white and red hyperplastic or nodular surfaces. Predominant locations for such lesions include the tongue, retro-angular mucosa, and buccal mucosa. This paper aims to investigate the potential influence of specific anatomical locations, retro-angular mucosa, on the development and occurrence of CHC. By examining the relationship between risk factors, we present an approach based on machine learning (ML) to predict the location of CHC occurrence. In this way, we employ Gradient Boosting Regression (GBR) to classify CHC lesion locations based on important risk factors. This estimator can serve both research and diagnostic purposes effectively. The findings underscore that the proposed ML technique can be used to predict the occurrence of CHC in retro-angular mucosa compared to other locations. The results also show a high rate of accuracy in predicting lesion locations. Performance assessment relies on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2), and Mean Absolute Error (MAE), consistently revealing favorable results that underscore the robustness and dependability of our classification method. Our research contributes valuable insights to the field, enhancing diagnostic accuracy and informing treatment strategies.
- Publikační typ
- časopisecké články MeSH
Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images.
Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.
This paper deals with transformations from electrocardiographic (ECG) to vectorcardiographic (VCG) leads. VCG provides better sensitivity, for example for the detection of myocardial infarction, ischemia, and hypertrophy. However, in clinical practice, measurement of VCG is not usually used because it requires additional electrodes placed on the patient's body. Instead, mathematical transformations are used for deriving VCG from 12-leads ECG. In this work, Kors quasi-orthogonal transformation, inverse Dower transformation, Kors regression transformation, and linear regression-based transformations for deriving P wave (PLSV) and QRS complex (QLSV) are implemented and compared. These transformation methods were not yet compared before, so we have selected them for this paper. Transformation methods were compared for the data from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated using a mean squared error (MSE) and a correlation coefficient (R) between the derived and directly measured Frank's leads. Based on the statistical analysis, Kors regression transformation was significantly more accurate for the derivation of the X and Y leads than the others. For the Z lead, there were no statistically significant differences in the medians between Kors regression transformation and the PLSV and QLSV methods. This paper thoroughly compared multiple VCG transformation methods to conventional VCG Frank's orthogonal lead system, used in clinical practice.
- MeSH
- databáze faktografické MeSH
- diagnóza počítačová metody MeSH
- elektrokardiografie metody MeSH
- lidé MeSH
- lineární modely MeSH
- matematické výpočty počítačové MeSH
- nemoci srdce diagnóza MeSH
- počítačové zpracování signálu * MeSH
- vektorkardiografie metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- srovnávací studie MeSH
AIM: This paper describes the digital implementation of a mathematical transform namely 2D Fast Discrete Curvelet Transform (FDCT) via UnequiSpaced Fast Fourier Transform (USFFT) in combination with the novel segmentation method for effective detection of breast cancer. METHODS: USFFT performs exact reconstructions with high image clarity. Radon, ridgelet and Cartesian filters are included in this method. Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) were calculated for the image and the resulting value showed that the proposed method performs well on mammogram image in reducing noise with good extraction of edges. This work includes a novel segmentation method, which combines Modified Local Range Modification (MLRM) and Laplacian of Gaussian (LoG) edge detection method to segment the textured features in the mammogram image. RESULTS: The result was analyzed using a Receiver Operating Characteristics (ROC) plot and the detection accuracy found was 99% which is good compared to existing methods.
Background: Microarray technologies are used to measure the simultaneous expression of a certain set of thousands of genes based on ribonucleic acid (RNA) obtained from a biological sample. We are interested in several statistical analyses such as 1) finding differentially expressed genes between or among several experimental groups, 2) finding a small number of genes allowing for the correct classification of a sample in a certain group, and 3) finding relations among genes. Objectives: Gene expression data are high dimensional, and this fact complicates their analysis because we are able to perform only a few samples (e.g. the peripheral blood from a limited number of patients) for a certain set of thousands of genes. The main purpose of this paper is to present the shrinkage estimator and show its application in different statistical analyses. Methods: The shrinkage approach relates to the shift of a certain value of a classic estimator towards a certain value of a specified target estimator. More precisely, the shrinkage estimator is the weighted average of the classic estimator and the target estimator. Results: The benefit of the shrinkage estimator is that it improves the mean squared error (MSE) as compared to a classic estimator. The MSE combines the measure of an estimator’s bias away from its true unknown value and the measure of the estimator’s variability. The shrinkage estimator is a biased estimator but has a lower variability. Conclusions: The shrinkage estimator can be considered as a promising estimator for analyzing high dimensional gene expression data.
- MeSH
- exprese genu * genetika MeSH
- lidé MeSH
- mikročipová analýza * metody statistika a číselné údaje MeSH
- RNA * genetika MeSH
- statistické modely MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
... uniqueness of exact solutions 45 -- 1.7.3 Explicit solutions for consistent data 47 -- 1.7.4 Least-squares ... ... 7.4.1 System matrix 341 -- 7.4.2 Adjoint operator and singular-value decomposition 344 -- 7.4.3 Image errors ... ... PROCESSES 649 -- 11.3.1 Temporal point processes 649 -- 11.3.2 Spatial point processes 651 -- 11.3.3 Mean ... ... Detectability in continuous data 863 -- 13.3 ESTIMATION THEORY 873 -- 13.3.1 Basic concepts 874 -- 13.3.2 MSE ...
Wiley series in pure and applied optics
[1st ed.] xli, 1540 s. : il.
