Medical Data Processing and Analysis
Medical data can be defined as obtaining information from patients (such as signals, images, sounds, chemical components and their concentration, body temperature, respiratory rate, blood pressure, and different treatment measurements) to quantify the patient’s status and disease stage. Computer-aid...
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| Format: | Online |
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| Jezik: | angleščina |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Teme: | |
| Online dostop: | ONIX_20230808_9783036580685_5 |
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| _version_ | 1869522567585333248 |
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| collection | Directory of Open Access Books |
| description | Medical data can be defined as obtaining information from patients (such as signals, images, sounds, chemical components and their concentration, body temperature, respiratory rate, blood pressure, and different treatment measurements) to quantify the patient’s status and disease stage. Computer-aided diagnostic (CAD) systems use classical image processing, computer vision, machine learning, and deep learning methods for image analysis. Using image classification or segmentation algorithms, they find a region of interest (ROI) pointing to a specific location within the given image or an outcome of interest in the form of a label pointing to a diagnosis or prognosis. Computer science, with the evolution of artificial intelligence and machine learning techniques, facilitates the modeling and interpretation of results—from carrying out measurements to experiments and observations. Employing technological tools for collection, processing, and analysis incorporates understanding the patient’s status and developing the treatment plan. Achieving highly accurate models requires a huge dataset. This issue can be solved by having enough knowledge around medical data processing and their analysis. This reprint shows state-of-the-art research in the field of medical data processing and analysis. The medical data are represented in signals, images, raw data, protein sequences, etc. Processing and analysis of any kind can indicate specific issues in the medical sector such as diagnosis, detection, prediction, and segmentation to enhance the visualization of the processed data |
| format | Online |
| id | doab-20.500.12854ir-112437 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1124372024-04-11T15:11:05Z Medical Data Processing and Analysis Mustafa, Wan Azani Alquran, Hiam atrial fibrillation perfect matrix of Lagrange differences statistical indicator decision support system deep learning heart failure mortality risk prediction time-varying covariates motor imagery Isolation Forest anomaly detection EEG signals classification PIMA dataset Type-2 diabetes Recurrent Neural Networks weight optimization Hamlet Pattern protein sequence classification SARS-CoV-2 bioinformatics machine learning ensemble learning heart disease ECG iris-spectrogram scalogram CNN ResNet101 ShuffleNet heart rhythm H. pylori atrophic gastritis convolution neural network feature fusion Canonical Correlation Analysis ReliefF generalized additive model diabetes mellitus blood glucose prediction forecasting long short-term memory nature-inspired feature selection leukemia white blood cell classification medical imaging breast cancer histopathological image review COVID-19 pandemic hybrid models public health accuracy and efficiency n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Medical data can be defined as obtaining information from patients (such as signals, images, sounds, chemical components and their concentration, body temperature, respiratory rate, blood pressure, and different treatment measurements) to quantify the patient’s status and disease stage. Computer-aided diagnostic (CAD) systems use classical image processing, computer vision, machine learning, and deep learning methods for image analysis. Using image classification or segmentation algorithms, they find a region of interest (ROI) pointing to a specific location within the given image or an outcome of interest in the form of a label pointing to a diagnosis or prognosis. Computer science, with the evolution of artificial intelligence and machine learning techniques, facilitates the modeling and interpretation of results—from carrying out measurements to experiments and observations. Employing technological tools for collection, processing, and analysis incorporates understanding the patient’s status and developing the treatment plan. Achieving highly accurate models requires a huge dataset. This issue can be solved by having enough knowledge around medical data processing and their analysis. This reprint shows state-of-the-art research in the field of medical data processing and analysis. The medical data are represented in signals, images, raw data, protein sequences, etc. Processing and analysis of any kind can indicate specific issues in the medical sector such as diagnosis, detection, prediction, and segmentation to enhance the visualization of the processed data 2023-08-08T15:11:08Z 2023-08-08T15:11:08Z 2023 book ONIX_20230808_9783036580685_5 9783036580685 9783036580692 https://directory.doabooks.org/handle/20.500.12854/112437 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7550 https://mdpi.com/books/pdfview/book/7550 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-8069-2 10.3390/books978-3-0365-8069-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036580685 9783036580692 254 Basel open access |
| spellingShingle | atrial fibrillation perfect matrix of Lagrange differences statistical indicator decision support system deep learning heart failure mortality risk prediction time-varying covariates motor imagery Isolation Forest anomaly detection EEG signals classification PIMA dataset Type-2 diabetes Recurrent Neural Networks weight optimization Hamlet Pattern protein sequence classification SARS-CoV-2 bioinformatics machine learning ensemble learning heart disease ECG iris-spectrogram scalogram CNN ResNet101 ShuffleNet heart rhythm H. pylori atrophic gastritis convolution neural network feature fusion Canonical Correlation Analysis ReliefF generalized additive model diabetes mellitus blood glucose prediction forecasting long short-term memory nature-inspired feature selection leukemia white blood cell classification medical imaging breast cancer histopathological image review COVID-19 pandemic hybrid models public health accuracy and efficiency n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Medical Data Processing and Analysis |
| title | Medical Data Processing and Analysis |
| title_full | Medical Data Processing and Analysis |
| title_fullStr | Medical Data Processing and Analysis |
| title_full_unstemmed | Medical Data Processing and Analysis |
| title_short | Medical Data Processing and Analysis |
| title_sort | medical data processing and analysis |
| topic | atrial fibrillation perfect matrix of Lagrange differences statistical indicator decision support system deep learning heart failure mortality risk prediction time-varying covariates motor imagery Isolation Forest anomaly detection EEG signals classification PIMA dataset Type-2 diabetes Recurrent Neural Networks weight optimization Hamlet Pattern protein sequence classification SARS-CoV-2 bioinformatics machine learning ensemble learning heart disease ECG iris-spectrogram scalogram CNN ResNet101 ShuffleNet heart rhythm H. pylori atrophic gastritis convolution neural network feature fusion Canonical Correlation Analysis ReliefF generalized additive model diabetes mellitus blood glucose prediction forecasting long short-term memory nature-inspired feature selection leukemia white blood cell classification medical imaging breast cancer histopathological image review COVID-19 pandemic hybrid models public health accuracy and efficiency n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| topic_facet | atrial fibrillation perfect matrix of Lagrange differences statistical indicator decision support system deep learning heart failure mortality risk prediction time-varying covariates motor imagery Isolation Forest anomaly detection EEG signals classification PIMA dataset Type-2 diabetes Recurrent Neural Networks weight optimization Hamlet Pattern protein sequence classification SARS-CoV-2 bioinformatics machine learning ensemble learning heart disease ECG iris-spectrogram scalogram CNN ResNet101 ShuffleNet heart rhythm H. pylori atrophic gastritis convolution neural network feature fusion Canonical Correlation Analysis ReliefF generalized additive model diabetes mellitus blood glucose prediction forecasting long short-term memory nature-inspired feature selection leukemia white blood cell classification medical imaging breast cancer histopathological image review COVID-19 pandemic hybrid models public health accuracy and efficiency n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| url | ONIX_20230808_9783036580685_5 |