A novel approach for breast ultrasound classification using two-dimensional empirical mode decomposition and multiple features

Aim: Breast cancer stands as a prominent cause of female mortality on a global scale, underscoring the critical need for precise and efficient diagnostic techniques. This research significantly enriches the body of knowledge pertaining to breast cancer classification, especially when employing breast ultrasound images, by introducing a novel method rooted in the two dimensional empirical mode decomposition (biEMD) method. In this study, an evaluation of the classification performance is proposed based on various texture features of breast ultrasound images and their corresponding biEMD subbands. Methods: A total of 437 benign and 210 malignant breast ultrasound images were analyzed, preprocessed, and decomposed into three biEMD sub-bands. A variety of features, including the Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), and Histogram of Oriented Gradient (HOG), were extracted, and a feature selection process was performed using the least absolute shrinkage and selection operator method. The study employed GLCM, LBP and HOG, and machine learning techniques, including artificial neural networks (ANN), k-nearest neighbors (kNN), the ensemble method, and statistical discriminant analysis, to classify benign and malignant cases. The classification performance, measured through Area Under the Curve (AUC), accuracy, and F1 score, was evaluated using a 10-fold cross-validation approach. Results: The study showed that using the ANN method and hybrid features (GLCM+LBP+HOG) from BUS images' biEMD sub-bands led to excellent performance, with an AUC of 0.9945, an accuracy of 0.9644, and an F1 score of 0.9668. This has revealed the effectiveness of the biEMD method for classifying breast tumor types from ultrasound images. Conclusion: The obtained results have revealed the effectiveness of the biEMD method for classifying breast tumor types from ultrasound images, demonstrating high-performance classification using the proposed approach

Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds

This paper proposed various feature extraction procedures to separate crackles and rhonchi of pathological lung sounds from normal lung sounds. The feature extraction process for distinguishing crackles and rhonchus from normal sounds comprises three signal-processing modules with the following functions: (1) f(min)/f(max) was the frequency ratio from the conventional technique of power spectral density (PSD) based on the Welch method. (2) The average instantaneous frequency (IF) and the exchange time of the instantaneous frequency were calculated by the Hilbert Huang transform (HHT). (3) The eigenvalues were obtained from the singular spectrum analysis (SSA) method. In the classification process, a support vector machine (SVM) was used to distinguish the crackles, rhonchus and normal lung sounds. The results showed that the selected features positively represented the characteristic changes in sounds. The PSD frequency ratio and the eigenvalues demonstrate higher classification accuracy (between 90% and 100%) than the calculations of average and exchange time of IF. The calculated features are extremely promising for the evaluation and classification of other biomedical signals as well as other lung sounds. (C) 2014 Elsevier Inc. All rights reserved

Can Functional Connectivity at Resting Brain in ADHD Indicate the Impairments in Sensory-Motor Functions and Face/Emotion Recognition?

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disease known to cause impair-ments in cognitive, sensory-motor functions and face/emotion recognition. This study aimed to examine the resting-state brain networks in children with ADHD using functional magnetic resonance imaging. We performed seed-to-voxel and region of interest (ROI) analyses including all Broadmann areas (BAs) comprehensively. Thirty right-handed children aged between 9 and 16 years (15 with ADHD and 15 typically developing control subjects closely matched for age and gender) were included. Ninety five brain regions including 84 BAs and 11 Default Mode network (DMN)-related regions (rsREL) were studied using seed-based and ROI-to-ROI analysis and connectivity measures were calculated (p < 0.001). Between-group differences were assessed by using t-statistics (p < 0.05). Seed-based analysis showed connectivity differences in the sensory-motor and face/emotion recognition regions in both groups. The between-group whole-brain comparison showed greater magnitude of activation in children with ADHD than in control subjects in brain regions that included the face/emotion recognition system and prefrontal cortex based on ROI-to-ROI analysis. This work revealed that the sensory-motor regions and regions related to face/emotion recognition showed atypical functional connectivities in ADHD patients compared to the controls. Observation of the differences in these regions supports previous findings in the literature based on task-based functional magnetic resonance imaging (fMRI) studies. Our study exhibited that these atypical differences can also occur in the resting brain. These results suggest that further investigations of regions related to motor-sensory and face/emotion recognition are required to better understand ADHD

Default mode network activity and neuropsychological profile in male children and adolescents with attention deficit hyperactivity disorder and conduct disorder

It is known that patients with Attention Deficit and Hyperactivity disorder (ADHD) and Conduct disorder (CD) commonly shows greater symptom severity than those with ADHD alone and worse outcomes. This study researches whether Default mode network (DMN) is altered in adolescents with ADHD + CD, relative to ADHD alone and controls or not. Ten medication-na < ve boys with ADHD + CD, ten medication-na < ve boys with ADHD and 10-age-matched typically developing (TD) controls underwent functional magnetic resonance imaging (fMRI) scans in the resting state and neuropsychological tasks such as the Wisconsin Card Sorting Test (WCST), Stroop Test TBAG Form (STP), Auditory Verbal learning Test (AVLT), Visual Auditory Digit Span B (VADS B) were applied to all the subjects included. fMRI scans can be used only nine patients in each groups. The findings revealed group differences between cingulate cortex and primary mortor cortex; cingulate cortex and somatosensory association cortex; angular gyrus (AG) and dorsal posterior cingulate cortex, in these networks increased activity was observed in participants with ADHD + CD compared with the ADHD. We found that lower resting state (rs)-activity was observed between left AG and dorsal posterior cingulate cortex, whereas higher rs-activity connectivity were detected between right AG and somatosensory association cortex in ADHD relative to the ones with ADHD + CD. In neuropsyhcological tasks, ADHD + CD group showed poor performance in WISC-R, WCST, Stroop, AVLT tasks compared to TDs. The ADHD + CD group displayed rs-functional abnormalities in DMN. Our results suggest that abnormalities in the intrinsic activity of resting state networks may contribute to the etiology of CD and poor prognosis of ADHD + CD