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NeXtSwin-X: dual-branch cross-attention fusion of ConvNeXt and swin transformer for accurate brain tumor classification from MRI and CT
Histotripsy dose impacts tumor cellular damage and treatment outcomes in a preclinical model of hepatocellular carcinoma
Enhancing biomedical signals through genetic algorithm optimized Exponentiated transmuted weibull denoising techniques
Abstract Biomedical signals are frequently corrupted by physiological and environmental noise, which obscures diagnostically relevant features and complicates clinical interpretation. This study introduces a denoising framework that integrates the Exponentiated Transmuted Weibull Distribution (ETWD) with Independent Component Analysis (ICA) to model complex, non-Gaussian noise patterns. The ETWD’s tri-parametric structure $$\:\left(\varvec{\alpha\:},\varvec{\beta\:},\varvec{\lambda\:}\right)$$ generalizes eleven classical distributions, enabling data-driven adaptability beyond conventional heavy-tailed models. A novel score function derived from ETWD is embedded within the FastICA algorithm, with parameters optimized via a Genetic Algorithm (GA). Sparsity constraints in the wavelet domain are applied to preserve transient signal features while suppressing noise. The framework is evaluated on electroencephalogram (EEG), electrocardiogram (ECG), and medical imaging datasets using standardized protocols with 70/30 development/test splits and 10 independent runs. Results demonstrate statistically significant improvements over conventional methods (Gauss, Pow3, Skew, Tanh). For EEG, Sparse ETWD achieved SNR of 7.77 ± 0.07 dB ( $$\:\varvec{p}<0.01$$ ) and improved epileptic spike detection accuracy from 71% to 94%. For ECG, ETWD achieved SNR of 21.34 ± 0.12 dB ( $$\:\varvec{p}<0.01$$ ) and improved R-peak detection F1-score from 0.89 to 0.97. For medical images, after correcting the evaluation protocol for normalized data (PSNR = $$\:-10{\mathbf{l}\mathbf{o}\mathbf{g}}_{10}\left(\text{MSE}\right)$$ ), ETWD achieved 32.01 dB under Gaussian noise, outperforming baselines across speckle (29.84 dB) and Rician noise (30.92 dB). Cross-dataset validation confirmed robustness within each modality, and comparison with a convolutional autoencoder under identical conditions showed competitive or superior performance without requiring training data. The framework offers a training-free, computationally efficient alternative to deep learning methods, and its application led to improved performance in downstream tasks such as R-peak and spike detection.
A multi-scale ensemble machine learning framework for assessing human–elephant conflict in the Brahmaputra flood plain
New adaptive memory stochastic fractional operators and their applications in computational intelligence
Research on vibration control and nonlinear dynamic behavior of railway vehicle systems based on nonlinear energy sinks
Targeting central immune signaling enhances the effects of methylphenidate in alleviating apathy-like behavior in 5xFAD mice
Metagenomic profiling unveils the viral diversity in field-collected Aedes larvae from Central India employing nanopore sequencing
Exploring potential VEGF receptor 2 inhibitors: a molecular modeling and pharmacophore-based screening approach
Phytochemical composition and in vitro anthelmintic activity of Erigeron floribundus (asteraceae) on three-stages of Haemonchus contortus of small ruminants
A diagnostic model for discrimination between Pneumocystis jirovecii pneumonia and colonization based on multiple parameters
S$$\vphantom{0}^{2}$$A-RConvNet: standalone self-attention enabled deep learning model for brain tumor classification with MRI images
Abstract Globally, the main factor that contributes to increasing the mortality rate among people is the development of abnormal cells in the brain, which leads to a Brain Tumor (BT). Therefore, the classification of BT is essential to prevent the increasing death rate by diagnosing the tumor based on its type. In order to classify the types of BT, several models are introduced, but they possess numerous drawbacks, including poor accuracy, higher time consumption, computational complexities, overfitting, and so forth. Hence, the Standalone Self-Attention based Repeated Convolutional Network ( $$\hbox {S}^{2}$$ S 2 A-RConvNet) model is developed to classify the BT types accurately to save the lives of affected people by solving the limitations of conventional approaches. The incorporation of the Standalone Self-Attention ( $$\hbox {S}^{2}$$ S 2 A) module enables the RConvNet to focus more on the tumor area, which helps to increase the model’s accuracy in BT categorization. Furthermore, the extraction of Structured ResNet Attention Gray-level (SRAG) features increases the training period and decreases the computational complexities, which leads to better performance of the model in BT classification. The $$\hbox {S}^{2}$$ S 2 A-RConvNet model attained the values of sensitivity of 97.61%, precision of 98.71%, F1-Score of 98.16%, specificity of 98.43% and accuracy of 97.98% with 90% of training using the BraTS 2021 dataset.