Alzheimer's disease is the most common neurodegenerative disorder among dementia, characterized by slow disease progression and complex imaging features. Traditional image-based diagnostic processes are time-consuming and vary in accuracy. To address these challenges, this study proposes a novel classification method based on hybrid attention and multi-scale information fusion (3D HAMSNet). The method leverages image data and a convolutional neural network to enhance the model's attention to the hippocampus, amygdala, and temporal lobe through the introduction of a hybrid attention mechanism. Additionally, it integrates multiscale spatial scale features of Alzheimer's disease by using a multiscale information fusion module based on dilated convolution and soft attention, enhancing early diagnosis and prediction. Finally, tested on 198 Alzheimer's patients, 200 individuals with mild cognitive impairment, and 139 healthy controls, it achieved 94.14% accuracy, 97.07% specificity, and 94.17% F1 score—represented improvements of 9.88%, 4.94%, and 10.17% over the baseline. The method outperforms existing classification methods and provides a new approach for early Alzheimer's diagnosis.

Due to the extreme similarity in imaging manifestations and locations of onset between meningiomas and acoustic neuromas in the CPA (cerebellopontine angle) region of the human body, clinical diagnosis is prone to misdiagnosis. Establishing an automatic tumor detection model using deep learning methods can effectively reduce the subjectivity of manual diagnosis, decrease missed diagnosis rates, and improve work efficiency. The diversity of datasets and superiority of image quality largely determine the performance of the detection model. This paper proposes a DCGAN (deep convolutional generative adversarial networks) with improved loss function for data augmentation of meningioma and acoustic neuroma detection models to address the issues of scarce medical image datasets, imbalanced number of categories, and poor imaging quality. Compared with traditional dataset augmentation methods, the results show that after optimizing the dataset with DCGAN, the accuracy, specificity, and mAP (mean average precision) of the brain tumor detection model increase by 0.014 6, 0.022 4, and 0.030 0 respectively compared to the original dataset, reaching 0.932 8, 0.898 6, and 0.930 0. The study demonstrates that optimizing datasets with DCGAN can significantly improve the performance of the brain tumor detection model, providing a more reliable tool for clinical medical diagnosis.

The morbidity and mortality rates of liver cancer in China remain concerning, emphasizing the urgent need for early diagnosis and treatment to improve the situation. In response to this challenge, we propose a novel 3D ELD_MobileNetV2 hepatic nodule classification model that incorporates attention mechanism and dilated convolution. This model is specifically designed to classify abdominal dynamic enhanced magnetic resonance images. Firstly, the two-dimensional network structure was extended to three dimensions to avoid the loss of spatial information during feature extraction of magnetic resonance imaging (MRI). Secondly, based on the local cross-channel interaction strategy, an efficient channel attention mechanism combining local features and global features was embedded in the bottleneck structure of MobileNetV2 network to enhance the key feature extraction capability. Then, 3D dilated structure was introduced into depthwise convolution to improve the receptive field of the convolution kernel. Meanwhile, the original activation function was replaced with Leaky ReLU6 activation function to improve the model's robustness. The model was tested and validated on a dataset comprising 120 patients (30 cases each of hepatitis, cirrhotic nodules, dysplastic nodules, and hepatocellular carcinoma). Experimental results demonstrate significant improvements over the original MobileNetV2, with an accuracy increase of 0.083. Compared to other networks, including AlexNet, VggNet16, ResNet50, ConvNeXt, the 3D ELD_MobileNetV2 achieves superior performance, with the accuracy of 0.792, F1_Score of 0.688, 0.750, 0.848, 0.872, micro-average AUC of 0.954, and macro-average AUC of 0.948. The findings highlight the effectiveness of the proposed model in classifying liver nodules across different stages. This advancement is expected to facilitate early diagnosis of liver cancer and improve clinical outcomes.

Due to the complex structure and small size of the hippocampus, precise segmentation of the hippocampus remains challenging. To address this issue, this study proposes a generative adversarial network (GAN) based on global and local feature information (GLGAN) for hippocampus segmentation. First, to improve network stability and segmentation accuracy while reducing the likelihood of problems such as information loss and gradient explosion, we proposed the global GAN (GGAN) by optimizing the generator and loss function of GAN. Second, since the discriminator is essentially a binary classifier and is not sensitive to small local changes, we introduced a GAN method of dual discriminator network structure that integrates both global and local feature information. Finally, a total loss function was designed to balance GAN adversarial loss and 3D u-net segmentation loss. The experimental results show that proposed method based on GLGAN facilitates intensive evaluation of the hippocampus, and drives the discriminator to push the mask value provided by the generator to a more realistic distribution, thereby enhancing hippocampus segmentation accuracy. The Dice coefficient and IOU for hippocampus segmentation using GLGAN are 0.804 and 0.672 respectively.

Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disease primarily diagnosed through diffusion-weighted imaging (DWI). However, the limitation of human visual interpretation and clinical experience can lead to inaccuracies in diagnosis. This research proposes a deep learning method based on cross self-supervision, alongside the construction of Co-ResNet50 and CO-ViT models for intelligent auxiliary diagnosis of NIID. This method uses self-supervised learning and effectively combines the characteristics of ResNet50 and ViT networks to improve the model’s feature extraction capabilities. The experiment preprocessed 249 DWI data and divided them into 204 training sets and 45 test sets. The results reveal that the CO-ResNet50 model has the best performance, with an accuracy of 95.49%, precision of 95.51%, recall of 95.44%, F1 score of 0.954 7, and AUC of 0.989 7. These findings underscore the model's potential to provide support for clinical NIID diagnosis.

Accurate and reliable diagnosis of cervical cancer is crucial for effective clinical treatment and prognosis evaluation. Given the low sensitivity of existing serum estrogen and tumor markers in assessing lesion features like parauterine invasion and lymph node metastasis, this study proposes a diagnostic method integrating multi-phase dynamic enhanced magnetic resonance imaging (DCE-MRI). Firstly, serum estradiol (E2), follicle-stimulating hormone (FSH), luteinizing hormone (LH), glycochain antigen 125 (CA125), glycochain antigen 19-9 (CA19-9) and DCE-MRI were detected to analyze the correlation between cervical cancer stage and serum levels of E2, FSH, LH, CA125 and CA19-9. Then, cervical biopsy results were used as the gold standard to analyze the diagnostic accuracy of DCE-MRI for cervical deep muscle invasion and lymph node metastasis. Finally, the serum levels of E2, FSH, LH, CA125, CA19-9, DCE-MRI diagnosis and combined diagnosis of cervical cancer were evaluated based on the receiver operating characteristic (ROC) curve. The experimental results showed that the AUC of combined diagnosis was 0.908, indicating that the combined diagnosis method effectively distinguished benign and malignant cervical lesions. Furthermore, DCE-MRI parameters K^trans and K^ep enriched the feature information, thereby improving the detection accuracy of lesions. This provides a novel approach for differentiating between benign and malignant cervical cancer and for determining its clinical stage.

A simulation model of the CW-EPR (continuous wave electron paramagnetic resonance) system’s signal transmission, modulation, and detection is constructed using the Simulink platform. The model supports signal source simulation, modulation, detection by the Schottky diode detector, and lock-in amplifier-assisted demodulation. Using this model, we characterize how 5,5-dimethyl-1-pyrroline N-oxide (DMPO) samples’ spectra vary with modulation amplitudes and phases. Consistency is observed between the simulated and experimentally measured spectral signals over a range of modulation amplitudes and phases. The presented simulation model offers theoretical support for understanding CW-EPR phenomenon in depth, optimizing experimental parameters, and guiding CW-EPR experiments. It also provides a reference for designing and optimizing EPR systems in subsequent research.

Acarbose, an α-glucosidase inhibitor, has been widely used in the treatment of type-II diabetes due to its unique mechanism of action. In this study, comprehensive and accurate ¹H and ¹³C NMR data of acarbose in DMSO-d₆, including ¹H NMR data of all hydroxyl groups of acarbose as well as ¹H and ¹³C NMR data of the dual signal of C-ring α and β, were reported for the first time. The assignment of ¹H and ¹³C NMR data was conducted using DEPT-135, ¹H-¹H COSY, ¹H-¹³C HSQC, and ¹H-¹³C HMBC techniques. The molecular structure of acarbose was confirmed, providing a reliable data basis for the safety and quality control of acarbose drugs.

Exercise intervention has become increasingly recognized as an effective method in aiding the rehabilitation of pediatric diseases and promoting structural and functional improvements of the pediatric brain. Magnetic resonance imaging (MRI) technology offers a range of analytical methods to study brain changes in children and is widely applied in pediatric exercise intervention research. In this paper, we summarize the findings and methodologies of the existing pediatric exercise intervention research and discuss the impact of exercise interventions on the pediatric brain. Additionally, we analyze the potential causes for the lack of significant MRI differences in some experiments and propose corresponding solutions. This review highlights the application of MRI technology in pediatric exercise intervention research, underscores its importance and potential value, and provides a reference for future studies in this field.

In recent years, significant progress has been made in tractography of superficial white matter in the brain based on diffusion magnetic resonance imaging. Superficial white matter fiber tracts serve as critical pathways connecting cortical and subcortical structures, playing a vital role in the construction of complete human connectome and neuropathological studies. This paper first summarizes the development of superficial white matter tractography techniques. Subsequently, it evaluates the strengths and weaknesses of different methods employed in superficial white matter fiber tract segmentation. Afterwards, the construction process of superficial white matter fiber tract atlas is discussed. Finally, this review concludes with an outlook on future research direction of superficial white matter tractography technology and segmentation methods.