Computer Aided Diagnosis Sensors

• Other Authors: El-Baz, Ayman (Editor), Giridharan, Guruprasad A. (Editor), Shalaby, Ahmed (Editor), Mahmoud, Ali H. (Editor), Ghazal, Mohammed (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Medicine; 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; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.