Epilepsy is a health problem that seriously affects the quality of humans for many years. Therefore, it important to accurately analyze and recognize epilepsy based on EEG signals, long time, researchers have attempted extract new features from signals recognition. However, very difficult select useful large number them in this diagnostic application. As development artificial intelligence progresses, unsupervised feature learning deep model can obtain better describe identified objects...

The accurate classification of Alzheimer's disease (AD) from MRI data holds great significance for facilitating early diagnosis and personalized treatment, ultimately leading to improved patient outcomes. To address this challenge, a comprehensive approach is proposed in study, which integrates advanced deep learning models. This study introduces an ensemble model AD classification, incorporates Soft-NMS into the Faster R–CNN architecture enhance candidate information merging improve...

In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. method, features are first searched and then combined with nonlinear features. Subsequently, these selected optimized to classify EEG signals. The extracted analyzed experimentally. by GAFDS show remarkable independence, they superior in terms ratio interclass distance intraclass distance. Moreover, can instantaneous frequency a signal...

Unmanned aerial vehicle (UAV) has been widely used in many industries. In the medical environment, especially some emergency situations, UAVs play an important role such as supply of medicines and blood with speed efficiency. this paper, we study problem multi-objective by situations. This is a complex that includes maintenance blood's temperature model during transportation, UAVs' scheduling routes' planning case multiple sites requesting blood, limited carrying capacity. Most importantly,...

As the Internet of medical Things emerge in field medicine, volume data is expanding rapidly and along with its variety. such, clustering an important procedure to mine vast data. Many swarm intelligence algorithms, such as particle optimization (PSO), firefly, cuckoo, bat, have been designed, which can be parallelized benefit mass computation. However, few studies focus on systematic analysis time complexities, effect instances (data size), attributes (dimensionality), number clusters,...

In recent years, the research on electroencephalography (EEG) has focused feature extraction of EEG signals. The development convenient and simple acquisition devices produced a variety signal sources diversity data. Thus, adaptability classification methods become significant. This study proposed deep network model for autonomous learning signals, which could self-adaptively classify signals with different sampling frequencies lengths. artificial design not obtain stable results when...

Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in brain. Since electroencephalogram (EEG) harmless and noninvasive detection method, it plays an important role neurological diseases. However, process analyzing EEG to detect diseases often difficult because electrical signals are random, non-stationary nonlinear.In order overcome such difficulty, this study aims develop new computer-aided scheme for automatic epileptic...

BACKGROUND: The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this Surface EMG (sEMG) can be monitored by electrodes on the skin surface and a reflection of neuromuscular activities. Therefore, we control limbs auxiliary equipment utilizing sEMG classification in order help physically disabled mouse. OBJECTIVE: To develop new method extract generated motion apply novel features classify sEMG. METHODS: A...

To overcome the complexity and variability of heart, along with factors like fuzzy boundaries low contrast produce automated segmentation based on machine learning. Segmentation cardiac MRI plays a vital role in various clinical applications. In this study, we propose an improved U-Net-CSP (Cross Stage Partial) method for accurate robust images.The architecture combines U-Net framework CSP module to enhance performance. The enables feature reuse mitigates overfitting issues associated deep...

Surface electromyography (sEMG) signal is the combined effect of superficial muscle EMG and neural electrical activity. In recent years, researchers did large amount human-machine system studies by using physiological signals as control signals.To develop test a new multi-classification method to improve performance analyzing sEMG based on public dataset.First, ten features were selected candidate features. Second, genetic algorithm (GA) was applied select representative from initial...

Osteosarcoma, the most common primary bone tumor originating from osteoblasts, poses a significant challenge in medical practice, particularly among adolescents. Conventional diagnostic methods heavily rely on manual analysis of magnetic resonance imaging (MRI) scans, which often fall short providing accurate and timely diagnosis. This underscores critical need for advancements technologies to improve detection characterization osteosarcoma.

Colonoscopy plays an important role in the clinical screening and management of colorectal cancer. The traditional 'see one, do teach one' training style for such invasive procedure is resource intensive ineffective. Given that colonoscopy difficult, time-consuming to master, use virtual reality simulators train gastroenterologists operations offers a promising alternative.

As the basic units of human body structure and function, cells have a considerable influence on maintaining normal work body. In medical diagnosis, cell examination is an important part understanding function. Incorporating into diagnosis would greatly improve efficiency pathological research patient treatment. addition, segmentation identification technology can be used to quantitatively analyze study cellular components at molecular level. It conducive pathogenesis diseases formulation...

Cloud computing has been widely used in every social field. The problem of energy consumption a cloud environment brought cost pressure to service providers and affected the natural environment. However, reasonable efficient scheduling resources could save lot for cluster. Meanwhile, it's necessary us take into account emergent needs consumer. So resource is often regarded as multi-objective with optimization time cost. We redefine this paper set up model, parallel improved on basis bee...

The highly parallel framework of Spark enables it to have outstanding advantages in accelerating computing speed. At the same time, artificial bee colony (ABC) algorithm needs improve its performance because high time complexity. In this paper, we combine ABC with platform. First, extend multi-objective (MOABC) algorithm; then implement MOABC based on We conduct several experiments using our proposed platform, and experimental results show that produces a better performance.

<title>Abstract</title> Background: Retinal blood vessels serve as crucial biomarkers for many ophthalmic and cardiovascular diseases. Therefore, the development of automatic segmentation models computer-aided diagnosis holds significant importance. However, existing CNN-based methods, such U-Net, face challenges in capturing long-range dependencies due to limited receptive fields inherent biases convolutional operations. Recently, numerous Transformer-based techniques have been integrated...