Parkinson’s disease patients have early vocal cord damage, and their voiceprint characteristics differ significantly from those of healthy individuals, which can be used to identify Parkinson's disease. However, the samples of the voiceprint dataset of Parkinson's disease patients are insufficient, so this paper proposes a double self-attention deep convolutional generative adversarial network model for sample enhancement to generate high-resolution spectrograms, based on which deep learning is used to recognize Parkinson’s disease. This model improves the texture clarity of samples by increasing network depth and combining gradient penalty and spectral normalization techniques, and a family of pure convolutional neural networks (ConvNeXt) classification network based on Transfer learning is constructed to extract voiceprint features and classify them, which improves the accuracy of Parkinson’s disease recognition. The validation experiments of the effectiveness of this paper’s algorithm are carried out on the Parkinson’s disease speech dataset. Compared with the pre-sample enhancement, the clarity of the samples generated by the proposed model in this paper as well as the Fréchet inception distance (FID) are improved, and the network model in this paper is able to achieve an accuracy of 98.8%. The results of this paper show that the Parkinson’s disease recognition algorithm based on double self-attention deep convolutional generative adversarial network sample enhancement can accurately distinguish between healthy individuals and Parkinson’s disease patients, which helps to solve the problem of insufficient samples for early recognition of voiceprint data in Parkinson’s disease. In summary, the method effectively improves the classification accuracy of small-sample Parkinson's disease speech dataset and provides an effective solution idea for early Parkinson's disease speech diagnosis.
Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, QAB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.
Effective medical image enhancement method can not only highlight the interested target and region, but also suppress the background and noise, thus improving the quality of the image and reducing the noise while keeping the original geometric structure, which contributes to easier diagnosis in disease based on the image enhanced. This article carries out research on strengthening methods of subtle structure in medical image nowadays, including images sharpening enhancement, rough sets and fuzzy sets, multi-scale geometrical analysis and differential operator. Finally, some commonly used quantitative evaluation criteria of image detail enhancement are given, and further research directions of fine structure enhancement of medical images are discussed.
The processing mechanism of the human brain for speech information is a significant source of inspiration for the study of speech enhancement technology. Attention and lateral inhibition are key mechanisms in auditory information processing that can selectively enhance specific information. Building on this, the study introduces a dual-branch U-Net that integrates lateral inhibition and feedback-driven attention mechanisms. Noisy speech signals input into the first branch of the U-Net led to the selective feedback of time-frequency units with high confidence. The generated activation layer gradients, in conjunction with the lateral inhibition mechanism, were utilized to calculate attention maps. These maps were then concatenated to the second branch of the U-Net, directing the network’s focus and achieving selective enhancement of auditory speech signals. The evaluation of the speech enhancement effect was conducted by utilising five metrics, including perceptual evaluation of speech quality. This method was compared horizontally with five other methods: Wiener, SEGAN, PHASEN, Demucs and GRN. The experimental results demonstrated that the proposed method improved speech signal enhancement capabilities in various noise scenarios by 18% to 21% compared to the baseline network across multiple performance metrics. This improvement was particularly notable in low signal-to-noise ratio conditions, where the proposed method exhibited a significant performance advantage over other methods. The speech enhancement technique based on lateral inhibition and feedback-driven attention mechanisms holds significant potential in auditory speech enhancement, making it suitable for clinical practices related to artificial cochleae and hearing aids.
ObjectiveTo compare the effectiveness of magnetic resonance spectroscopy (MRS) and Dynamic Contrast-enhancement (DCE-MRI) with 1.5 T MR scanner in diagnosing prostate cancer.
MethodsFrom April 2011 to December 2012, based on the results of biopsy, we measured 216 regions of interest (ROIs) in images of MRS and DCE-MRI, comprised of 131 ROIs from cancer zone and 85 ROIs from non-cancer zone. The data were analyzed with statistical methods, including receiver operating characteristic (ROC) curve.
ResultsThere were significant differences between the malignant group and the benign group (P<0.05) in Cit integral, Cho integral, CC/Cit ratio, the type of time-signal intensity curve, initial value, enhancement rate and ratio of enhancement. According to ROC curve, the area under curve (AUC) of CC/Cit and enhancement rate was 0.853 and 0.719, respectively. AUC of time to peak, time difference, enhancement rate and Cit integral was lower than 0.400. The optimal operating point (OOP) of CC/Cit was 0.775, with a specificity of 0.85 and a sensitivity of 0.79, and the AUC was 0.853. The OOP of the ratio of enhancement was 60.89, with a specificity of 0.66 and a sensitivity of 0.71, and the AUC was 0.719.
ConclusionMRS is more sensitive and specific than DCE-MRI to diagnose prostate cancer when an 1.5 T MR scanner is used. On the other hand, MRS is susceptible to interference, but DCE-MRI can make up for these deficiencies.
ObjectiveTo compare the effectiveness of T2 weighted image (T2WI) and some compounded MRI techniques, including T2WI combined with magnetic resonance spectroscopy (T2WI+MRS), T2WI combined with diffusion weighted imaging (T2WI+DWI) and T2WI combined with dynamic contrast-enhancement [T2WI+(DCE-MRI)] respectively, with 1.5 T MR scanner in diagnosing prostate cancer through a blinding method.
MethodsBetween March 2011 and April 2013, two observers diagnosed 59 cases with a blinding method. The research direction of radiologist A was to diagnose prostate cancer. The observers diagnosed and scored the cases with T2WI, T2WI+(DCE-MRI), T2WI+MRS, T2WI+DWI and compositive method respectively. The data were statistically analyzed with receiver operating characteristic (ROC) curve.
ResultsAccording to the ROC curve, both observers got the sequence of area under curve (AUC) as T2WI+DWI > T2WI+(DCE-MRI) > T2WI+MRS > T2WI. On the basis of the result from observer A, the AUC from each technique was similar. The AUC of T2+DWI was slightly bigger than others. The specificity of single T2WI was the lowest; the sensitivity of T2WI was slightly higher. The AUC of the compositive method was marginally larger than T2WI+DWI. According to the result from observer B, the AUC of T2WI+DWI was obviously larger than the others. The AUC of single T2WI was much smaller than the other techniques. The single T2WI method had the lowest sensitivity and the highest specificity. The AUC of T2WI+DWI was slightly larger than the compositive method. The AUC of T2WI+(DCE-MRI), T2WI+MRS, single T2WI methods from observer A was obviously higher than those from the score of observer B. The AUC of T2WI+DWI from the two observers was similar.
ConclusionThe method of combined T2WI and functional imaging sequences can improve the diagnosing specificity when a 1.5 T MR scanner is used. T2WI+DWI is the best method in diagnosing prostate cancer with least influence from the experience of observers in this research. The compositive method can improve the diagnosis of prostate cancer effectively, but when there are contradictions between different methods, the T2WI+DWI should be considered as a key factor.
ObjectiveTo investigate the diagnostic performance of parameters of arterial enhancement fraction (AEF) based on enhanced CT with histogram analysis in the severity of liver cirrhosis.MethodsThe patients with liver cirrhosis clinically confirmed and met the inclusion criteria were included from January 2016 to December 2018 in the First Affiliated Hospital of Chengdu Medical College, then them were divided into grade A, B, and C according to the Child-Pugh score. Meanwhile, the patients without liver disease were selected as the control group. All patients underwent the upper abdomen enhanced CT scan with three-phase and the biochemical examination of liver function. The parameters of AEF histogram were obtained by using the CT Kinetics software, and the aspartic aminotransferase and platelet ratio index (APRI) was calculated. The differences of parameters of AEF histogram and APRI among these patients with liver cirrhosis and without liver disease were analyzed. The diagnostic performance was evaluated by using the area under curve (AUC) of receivers operating characteristic curve.ResultsEighty-five patients with liver cirrhosis were included in this study, including 25, 41, and 19 patients with grade A, B, and C of Child-Pugh score, respectively, and there were 20 patients in the control group. The consistencies in measuring the parameters of AEF histogram twice for the same observer and between the two observers were good (intraclass correlation coefficient was 0.938 and 0.907, respectively). The mean, median, and kurtosis of AEF histogram and the APRI among the grade A, B, C of Child-Pugh score, and control group had significant differences (all P<0.001) and these indexes were positively correlated with the severity of liver cirrhosis (rs=0.811, P<0.001; rs=0.827, P<0.001; rs=0.731, P<0.001; rs=0.711, P<0.001). The AUC of the mean, median, kurtosis, and APRI in diagnosing grade A of liver cirrhosis was 0.829, 0.841, 0.747, and 0.718, respectively; which in diagnosing grade B of liver cirrhosis was 0.847, 0.734, 0.704, and 0.736, respectively; in diagnosing grade C of liver cirrhosis was 0.646, 0.825, 0.782, and 0.853, respectively.ConclusionThe mean and median of AEF histogram parameters based on enhanced CT with three-phase and serological APRI are useful in diagnosis of grage A, B, and C of liver cirrhosis, respectively.
Cognitive enhancement refers to the technology of enhancing or expanding the cognitive and emotional abilities of people without psychosis based on relevant knowledge of neurobiology. The common methods of cognitive enhancement include transcranial direct current stimulation (tDCS) and cognitive training (CT). tDCS takes effect quickly, with a short effective time, while CT takes longer to work, requiring several weeks of training, with a longer effective time. In recent years, some researchers have begun to use the method of tDCS combined with CT to regulate the cognitive function. This paper will sort out and summarize this topic from five aspects: perception, attention, working memory, decision-making and other cognitive abilities. Finally, the application prospect and challenges of technology are prospected.
In order to fully explore the neural oscillatory coupling characteristics of patients with mild cognitive impairment (MCI), this paper analyzed and compared the strength of the coupling characteristics for 28 MCI patients and 21 normal subjects under six different-frequency combinations. The results showed that the difference in the global phase synchronization index of cross-frequency coupling under δ-θ rhythm combination was statistically significant in the MCI group compared with the normal control group (P = 0.025, d = 0.398). To further validate this coupling feature, this paper proposed an optimized convolutional neural network model that incorporated a time-frequency data enhancement module and batch normalization layers to prevent overfitting while enhancing the robustness of the model. Based on this optimized model, with the phase locking value matrix of δ-θ rhythm combination as the single input feature, the diagnostic accuracy of MCI patients was (95.49 ± 4.15)%, sensitivity and specificity were (93.71 ± 7.21)% and (97.50 ± 5.34)%, respectively. The results showed that the characteristics of the phase locking value matrix under the combination of δ-θ rhythms can adequately reflect the cognitive status of MCI patients, which is helpful to assist the diagnosis of MCI.
Microphone array based methods are gradually applied in the front-end speech enhancement and speech recognition improvement for cochlear implant in recent years. By placing several microphones in different locations in space, this method can collect multi-channel signals containing a lot of spatial position and orientation information. Microphone array can also yield specific beamforming mode to enhance desired signal and suppress ambient noise, which is particularly suitable to be applied in face-to-face conversation for cochlear implant users. And its application value has attracted more and more attention from researchers. In this paper, we describe the principle of microphone array method, analyze the microphone array based speech enhancement technologies in present literature, and further present the technical difficulties and development trend.
