Computer Aided Diagnosis Sensors

doi:10.3390/books978-3-0365-9533-7

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Computer Aided Diagnosis Sensors

Edited by

November 2023

670 pages

ISBN978-3-0365-9532-0 (Hardback)
ISBN978-3-0365-9533-7 (PDF)

https://doi.org/10.3390/books978-3-0365-9533-7

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This book is a reprint of the Special Issue Computer Aided Diagnosis Sensors that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Summary

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.

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Keywords

prostate cancer; image processing; histopathology images; digital image analysis; computational pathology; artificial intelligence; image processing; Nosema disease; machine learning; deep learning; image; disease detection; blood flow velocity quantification; conjunctival microvessel; deep learning; image processing; motion correction; optical imaging system; vessel segmentation; prostate cancer; transfer learning; ALexNet; VGGNet; ADC maps; computer-aided diagnosis; convolutional neural networks; deep learning; 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; computer-aided diagnosis; sMRI; U-NET; deep learning; uveitis grading; OCT segmentation; computed tomography (CT); lung; chest; segmentation; COVID-19; autism; ASD; computer-aided diagnosis; deep learning; CNN; CWT; dendritic cells; electrical characterization; image processing; immune system; macrophages; COVID-19; chest X-ray; deep learning; convolutional neural networks; diagnosis; POCUS; multichannel system; channel data; bladder monitoring; POUR; machine-learning; segmentation; COVID-19; NC protein; optical detection; protein–protein interactions; RBD; SARS-CoV-2; deep learning; classification; grade groups; CAD system; prostate cancer; chewing; smart devices; discrete wavelet decomposition; low pass filter; number of chews; carotid intima-media thickness; IMT; CCA; segmentation; deep learning; encoder-decoder model; left ventricular assist devices; sensor-based control; pump independent; suction index; physiological perfusion; suction prevention; biomedical informatics; cardiovascular disease; deep learning; ECG; heart rate variability; machine learning; PPG; smartphones; smart wearables; thermal camera; non-contact spirometry; artificial intelligence regression; respiration signal; respiration rate mobile application; multiple object tracking; data association; dataset; deep learning; semantic attribute; autism spectrum disorder (ASD); DTI; neuroimaging; ABIDE-II; diagnosis; lung sound detection; heart sound detection; convolutional neural network; model fusion; multi-features; prostate cancer; MRI; texture analysis; shape features; functional features; computer-aided diagnosis; PSA; osteoporosis; strength training; osteopenia; bone mass; DEXA; diabetic retinopathy (DR); optical coherence tomography angiography (OCTA); convolutional neural networks (CNN); image encryption; security analysis; Alzheimer’s disease; deep learning; convolutional neural network (CNN); MRI; brain imaging; machine learning (ML); cervical cancer; human papillomavirus (HPV); gradient boosting; support vector machine (SVM); deep learning; skin lesions; skin cancer; melanoma; image classification; Diabetic Retinopathy; fundus images; lesions detection; deep learning; YOLO; n/a