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Sensors used to diagnose, monitor or treat diseases in the medical domain are known as medical sensors. There are several types of medical sensors that can be utilized for various applications, such as temperature probes, force sensors, pressure sensors, oximeters, electrocardiogram sensors that measure the electrical activity of the heart, heart rate sensors, electroencephalogram sensors that measure the electrical activity of the brain, electromyogram sensors that record electrical activity produced by skeletal muscles, and respiration rate sensors that count how many times the chest rises in a minute. The output of these sensors used to be interpreted by humans, which was time consuming and tedious; however, such interpretations became easy with advances in artificial intelligence (AI) techniques and the integration of the sensor outputs into computer-aided diagnostic (CAD) systems. This reprint presents some of the state-of-the-art AI approaches that are used to diagnose different diseases and disorders based on the data collected from different medical sensors. The ultimate goal is to develop comprehensive and automated computer-aided diagnosis by focusing on the different machine learning algorithms that can be used for this purpose as well as novel applications in the medical field.

Titel:	Computer Aided Diagnosis Sensors
Autor/in / Beteiligte Person:	El-Baz, Ayman ; Giridharan, Guruprasad A. ; Shalaby, Ahmed ; Mahmoud, Ali H. ; Ghazal, Mohammed
Link:	View record at Directory of Open Access Books (Volltext)
Veröffentlichung:	Basel: MDPI - Multidisciplinary Digital Publishing Institute, 2023
Medientyp:	E-Book
Umfang:	670
ISBN:	978-3-0365-9532-0 (print) ; 978-3-0365-9533-7 (print)
DOI:	10.3390/books978-3-0365-9533-7
Schlagwort:	prostate cancer image processing histopathology images digital image analysis computational pathology artificial intelligence Nosema disease machine learning deep learning image disease detection blood flow velocity quantification conjunctival microvessel motion correction optical imaging system vessel segmentation transfer learning ALexNet VGGNet ADC maps computer-aided diagnosis convolutional neural networks diabetic retinopathy diabetic retinopathy classification diabetic retinopathy lesions localization YOLO thyroid cancer CNN MRI DWI radiomics BITalino BrainAmp ICC intraclass correlation coefficient Bland–Altman method big healthcare data classification decision-making feature selection whale optimization naive bayes renal cell carcinoma CE-CT morphology texture functionality RC-CAD electrocardiogram (ECG) affective computing emotion recognition system healthcare Alzheimer’s disease personalized diagnosis mild cognitive impairment sMRI U-NET uveitis grading OCT segmentation computed tomography (CT) lung chest segmentation COVID-19 autism ASD CWT dendritic cells electrical characterization immune system macrophages chest X-ray diagnosis POCUS multichannel system channel data bladder monitoring POUR machine-learning NC protein optical detection protein–protein interactions RBD SARS-CoV-2 grade groups CAD system chewing smart devices discrete wavelet decomposition low pass filter number of chews carotid intima-media thickness IMT CCA encoder-decoder model left ventricular assist devices sensor-based control pump independent suction index physiological perfusion suction prevention biomedical informatics cardiovascular disease ECG heart rate variability PPG smartphones smart wearables thermal camera non-contact spirometry artificial intelligence regression respiration signal respiration rate mobile application multiple object tracking data association dataset semantic attribute autism spectrum disorder (ASD) DTI neuroimaging ABIDE-II lung sound detection heart sound detection convolutional neural network model fusion multi-features texture analysis shape features functional features PSA osteoporosis strength training osteopenia bone mass DEXA diabetic retinopathy (DR) optical coherence tomography angiography (OCTA) convolutional neural networks (CNN) image encryption security analysis convolutional neural network (CNN) brain imaging machine learning (ML) cervical cancer human papillomavirus (HPV) gradient boosting support vector machine (SVM) skin lesions skin cancer melanoma image classification Diabetic Retinopathy fundus images lesions detection
Sonstiges:	Nachgewiesen in: Directory of Open Access Books Sprachen: English Document Type: eBook File Description: application/octet-stream Language: English Rights: Attribution 4.0 International ; URL: https://creativecommons.org/licenses/by/4.0/ Notes: ONIX_20231130_9783036595320_292

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Computer Aided Diagnosis Sensors