Deep learning for heterogeneous medical data analysis

In recent days, many information and image evidence in the field of medicine is being designed and developed by the advancement of computer technology. Presently, sport medical data is an essential department for medical sector and it is responsible for assuring sports safety based on the recovery level after an injury due to sports activity. The approach to reliably interpretation and valuable data information using a vast number of medical sports data and events has become an important research path in the collection and analysis of medical data. This paper discusses the extraction, study, and lack of training and accuracy of complex algorithms for critical sporting medical data. This paper involves with an optimized convolutional neural network (OCNN) based on deep-learning model to ensure successful detection and risk assessments of sport-medicine diseases and adopts the Self-Adjustment Resizing algorithm (SAR) augmented by the self-coding method of the convolution (SCM). CNN model helps to evaluate sports medicine in multi-dimensional results and suggested OCNN classification constitutes two convolutional layers, two pool layers, a fully connected layer, and a SoftMax structure that can be used for the classification of sport-related medical data. The CNN facilitates multi-dimensional sports medicine data analysis and to conclude a cloud-based loop model to create an advanced medical data network for sports medicine. Experiments illustrate that this approach offers technical support and guide to deploying a specific cloud-based fusion system.

Classification is a data processing technique of a great significance both for native eHealth systems and web telemedicine solutions. In this sense, artificial neural networks have been widely applied in telerehabilitation as powerful tools to process information and acquire a new medical knowledge. But effective analysis of multidimensional heterogeneous medical data, still poses considerable difficulties. It was shown that processing too many data features simultaneously is costly and has some adverse effects on the resulting models classification properties. Therefore, there is a strong need to develop new techniques for selecting features from the very large data sets that include many irrelevant, or redundant features. This work addresses the context-related feature selection problem from medical data by proposing utility of Sigma-if neural network being an effective model of neurology patients's low-level distributed selective attention mechanisms. Our experiments indicate that a context-aware technique can reduce the average cost of medical data acquisition and data processing as well as it can decrease classification error probability resulting in increasing the overall eHealth systems reliability.

Medical organizations struggle to deal with huge high-dimensional datasets that need powerful machine learning systems to produce precise healthcare outcomes. Traditional analytical techniques prove inadequate when dealing with extraction from features and performance of classifiers in this specific setting. The research introduces an algorithm which enhances Stacked Autoencoders (SAEs) by combining them with a customized Logistic Regression model intended for medical high-dimensional data analysis. This approach implements a Hybrid Imputation Method using MICE and KNN Imputation which precedes other stages and helps process missing values and outliers in medical data. We use CNNs and SAEs together for deep feature extraction before using Feature Fusion to assemble a robust feature collection. A set of the most important features is identified by executing Advanced Ensemble Feature Selection (EFS) procedures which include Few-shot Learning and Model-Agnostic Meta-Learning Algorithm (MAML) and Genetic Algorithm-Based Feature Selection (GAFS). The procedure of fine-tuning pre-trained models represents an effective enhancement for classification tasks particularly in situations of limited dataset availability. The experimental outcomes demonstrate remarkable performance gains in terms of accuracy and sensitivity and specificity as well as reduced execution time as compared to current techniques. Upcoming work for this study involves speeding up algorithm processing abilities and scalability alongside the integration of robust deep learning structures with self-supervised learning methodologies together with upgrade transfer learning approaches for medical dataset variety applications. The study will concentrate on enhancing model transparency through explainable AI and real-time validation for clinical deployment and ethical and regulatory compliance to develop this technique for practical healthcare settings.

A large number of patient healthcare data have been collected in the process of diagnosis and treatment of intensive care medicine, which provides major benefits for patient safety and quality. Unfortunately, the application of medical data is greatly limited. Key barriers to the use of the data include difficulties in data extraction and cleaning, and the construction of high-quality data sets promotes the research of medical big data analysis. In China, there is few intensive care data set built by clinicians has been used for clinical outcome prediction. This study developed and evaluated an Intensive Care Medical (ICM) data set for critically care patients that can be used for deep learning. The ICM data set contained four types of data collected routinely in Chinese hospitals, including all-cause characteristics of administrative information, vital signs, laboratory tests, and intravenous medication records. A total of 17,291 ICU admissions involving 12,815 patients aged 14 years and older were extracted from the data set. Deep learning model achieved high accuracy for tasks in hospital mortality predicting (AUROC[area under the receiver operator curve] reach 0.8941). We believe that the ICM data set can be used to create accurate predictions for a variety of clinical scenarios.

The development of computer hardware allows rapid accumulation of medical imaging data. Deep learning has shown great potential in medical imaging data analysis and establish a new area of machine learning. The commonly used deep learning models were firstly introduced in the paper, and then, summarized with the application of deep learning in the detection, classification, diagnosis, segmentation, identification of medical imaging. The application of deep learning in oral and maxillofacial radiology and other discipline of stomatology was proposed. At the end, the paper discussed the problems of deep learning in medical imaging research.

Bone cancer is a significant health issue that leads to severe complications and deaths worldwide. Early detection can significantly improve patient outcomes, often resulting in a complete cure. Traditional approaches to managing bone cancer can be augmented with technology-driven methods, particularly those enabled by artificial intelligence. Convolutional Neural Networks (CNNs) and their variants have proven effective in medical data analysis. However, deep learning techniques require enhancements to improve performance in cancer detection across various medical imaging modalities. In this paper, we propose a deep-learning framework that utilizes advanced CNN models for the automatic screening of bone cancer. We have enhanced both the CNN model and the ResNet-50 model, which are integral components of the proposed framework. Additionally, we introduced an algorithm called Learning-based Bone Cancer Detection (LbBCD), designed to optimize the utilization of these enhanced deep learning models to improve bone cancer detection efficiency. Our research emphasizes a Region of Interest (ROI) based approach to enhance the screening process for bone cancer. Using a benchmark dataset known as the Bone CT Scan dataset, our empirical study demonstrated that the proposed deep learning framework, integrated with enhanced CNN and ResNet-50 models, achieved remarkable performance. Specifically, the enhanced CNN model reached an accuracy of 90.90%, while the enhanced ResNet-50 model achieved an accuracy of 92.20%, outperforming state-of-the-art deep learning models. Therefore, this proposed system can be integrated into healthcare applications for automatically screening bone cancer, contributing to a clinical decision support system for healthcare professionals.

With the rapid expansion of the medical information ecosystem, systematic processing of large-scale and heterogeneous medical data is essential for optimizing diagnosis, monitoring, and treatment protocols for complex neurological and oncological conditions such as tumor pathologies and ischemic stroke. Accurate evaluation of these diseases relies on the integration of structured and unstructured data sources, including electronic health records, genomic profiles, radiological images, and laboratory results. Transformer-based Large Language Models (LLMs) have significantly advanced natural language processing (NLP), enabling semantic interpretation of clinical texts. These models support the contextual understanding of histopathological descriptions, clinical protocols, contributing to the refinement of diagnostic algorithms in oncology and stroke. This article examines the architectural features, benefits, limitations, and future potential of LLMs in centralized medical data analysis. The aim is to enhance the role of LLMs as accurate analytical tools in clinical decision-making and to foster the development of personalized medical services.

Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and Ultrasound (US)) poses additional challenges in FS, necessitating advanced feature fusion strategies. Multi-modal feature fusion combines information from different imaging modalities to improve diagnostic accuracy. Recently, quantum computing has gained attention as a revolutionary approach for FS, providing the potential to handle high-dimensional medical data more efficiently. This systematic literature review comprehensively examines classical, Deep Learning (DL), hybrid, and quantum-based FS techniques in medical imaging. Key outcomes include a structured taxonomy of FS methods, a critical evaluation of their performance across modalities, and identification of core challenges such as computational burden, interpretability, and ethical considerations. Future research directions—such as explainable AI (XAI), federated learning, and quantum-enhanced FS—are also emphasized to bridge the current gaps. This review provides actionable insights for developing scalable, interpretable, and clinically applicable FS methods in the evolving landscape of medical imaging.

This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up‐to‐date medical learning models regarding both the computational and cost the quality of returned solutions.

It is of great significance to establish a scientific and perfect application model of medical and health big data analysis for promoting the application of medical and health big data. This paper mainly studies the computer-aided medical and health data analysis of Inner Mongolia Autonomous Region based on big data. This paper adopts literature research method, field research method and data science method. Literature research is used to understand big data platforms and their technologies, and to analyze the limitations of data analysis methods in traditional data warehouses. The application process of data analysis based on big data platform is studied by using data science method. The established medical and health data analysis application process based on the big data platform and the described medical and health big data application theme were consulted by using the expert consultation method, and they were modified and improved according to the expert opinions.KeywordsBig data platformComputer aidedHealthcare dataData analysis

From late 2019 to early 2020, the coronavirus outbreak affected 213 countries and territories around the world. This respiratory virus seriously affects human lung functionality. One way to diagnose this illness and find out if the lungs are infected is to evaluate chest X-ray. The evaluation of X-rays is challenging because corona has minor effects on the lungs in the early stages, and other diseases can have a similar effect. In this condition, Computer-Aided Diagnosis (CAD) can make a huge contribution and help decision support for healthcare professionals. Deep learning has obtained great results in data analysis recently, but the requirement for a large amount of training data prevents the use deep learning in medical data analysis since it is difficult to obtain a large amount of data from the medical field. This paper proposes an effective deep transfer learning-based model that improves current state-of-the-art systems in COVID-19 detection in chest radiographs. The weights of the DesneNet121 and ResNet50 on the Imagenet have transferred as initial weights, and then the two models have been fine-tuned with a deep classifier with data augmentation to detect three classes of COVID-19, Viral Pneumonia and normal radiographs. The proposed models obtained 97.83% accuracy with minimal false-negative results on the only public available COVID-19 radiography dataset. The Image-Level Accuracy (ILA) of the results outperform the results of previous studies, together with sensitivity and recall performance. Moreover, the proposed methods are scalable, and can be expanded to cover the detection of other types of diseases in the future and be integrated with more CNNs to increase their generalization capabilities.

Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy

In the latest progress in healthcare, the continuous creation of digital medical information is an important basis for the analysis of large medical data. This paper proposes a medical big data analysis algorithm based on access control system that provides reliable security protection to big medical data (BMD) by considering various quantitative parameters. The proposed algorithm calculates the reputation values of different users, which correspond to each parameter. Based on the calculated reputation values, the proposed algorithm grants access authority to each user. Simulation results are performed to verify the effectiveness of security system solving protection of the sensitive personal information of patients.

Purpose: The purpose of this article is to offer an overview of the recent applications of cloud computing in medical imaging and their advances in the field. It reviews existing scientific and academic literature on cloud computing-based platforms and solutions for medical image analysis and storage of medical imaging data. Materials and methods: This article uses available scientific literature on the applications of cloud computing in medical imaging from PubMed, Google Scholar and ScienceDirect. Results: The review shows that interest and research in cloud computing applications in medical imaging has increased in recent years. This has led to new and more effective platforms and solutions for medical image analysis and storage of medical imaging data. Innovative applications of cloud computing in medical imaging try to address ethical and security concerns using authentication, encryption, anonymization and access controls. Conclusions: Cloud computing offers promising applications in medical imaging. Notwithstanding, further research is necessary to demonstrate their effectiveness, safety and security in a medical setting.

Transforming the non-structured medical text data into structured data is the basis of the processing and analysis of medical data. The effect of general-purpose word segmentation tools recognizing terminology is not ideal, which greatly affects the accuracy of the word segmentation, and further influences the result of text structuralization. In view of above problems, this paper puts forward a method of discovering new words based on word embedding. It uses Google open source word vector tool word2vec to train text and map the words into abstracted n-dimensional vector space. We can get the latent semantic relations between words and words in the corpus. And then combining the information entropy and word frequency, we can find new words. Finally, we design information extraction rules to get the key information according to the new words, and organize them into structured data. Experimental results on real medical data show that the accuracy is improved by 10% compared to traditional method, and the time is saved by 18% compared to traditional method.