Artificial Intelligence in Biomedical and Modern Healthcare Informatics provides a deeper understanding of the current trends in AI and machine learning within healthcare diagnosis, its practical approach in healthcare, and gives insight into different wearable sensors and its device module to help doctors and their patients in enhanced healthcare system. The primary goal of this book is to detect difficulties and their solutions to medical practitioners for the early detection and prediction of any disease. The 56 chapters in the volume provide beginners and experts in the medical science field with general pictures and detailed descriptions of imaging and signal processing principles and clinical applications. With forefront applications and up-to-date analytical methods, this book captures the interests of colleagues in the medical imaging research field and is a valuable resource for healthcare professionals who wish to understand the principles and applications of signal and image processing and its related technologies in healthcare.
1. Chapter 8 Electroencephalography (EEG) in epilepsy care: An introduction - p. 78 2. Chapter 9 A review on brain–computer interface and its applications - p. 84 3. Chapter 10 Recent trends in metabolomics, machine learning and artificial intelligence - p. 92 4. Chapter 11 A comprehensive review on state-of-the-art imagined speech decoding techniques using electroencephalography - p. 102 5. Chapter 12 Parkinson's disease diagnosis, treatment, and future scope: An epilogue - p. 128 6. Chapter 13 Recent advances in removal of artifacts from EEG signal records - p. 136 7. Chapter 14 Computer-aided diagnosis in healthcare: Case study on lung cancer diagnosis - p. 146 8. Chapter 15 AI and its role in predictive preclinical models for drug efficacy testing - p. 158 9. Chapter 16 Machine learning–based solutions for brain tumor detection: Comparative study and limitations - p. 166 10. Chapter 17 Indoor and home-based poststroke rehabilitation techniques—A systemic review - p. 176 11. Chapter 18 A comprehensive study on implementable antennas for medical applications - p. 186 12. Chapter 19 Deep learning for bone age assessment: Current status and future prospects - p. 194 13. Chapter 20 Emerging applications of artificial intelligence in analyzing EEG signals for the healthcare sector - p. 204 14. Chapter 21 Epilepsy detection system using CWT and deep CNN - p. 212 15. Chapter 22 Isolated Indian Sign Language Recognition with multihead attention transformer based network and MediaPipe's landmarks - p. 224 16. Chapter 23 Diagnosis of Parkinson's disease based on biological and imaging-derived features using machine learning and deep learning - p. 236 17. Chapter 24 Braintumor and feature detection from MRI and CT scan using artificial intelligence - p. 246 18. Chapter 25 Neuromodulation via brain stimulation: A promising therapeutic perspective for Alzheimer's disease - p. 258 19. Chapter 26 A biosensor for the detection of viruses using one-dimensional photonic crystals - p. 268 20. Chapter 27 Artificial intelligence–based seizure detection systems in electroencephalography: Transforming healthcare for accurate diagnosis and treatment - p. 278 21. Chapter 28 Artificial intelligence and image enhancement–based methodologies used for detection of tumor in MRIs of human brain - p. 290 22. Chapter 29 Machine learning-based workload identification using functional near-infrared spectroscopy (fNIRS) data - p. 300 23. Chapter 30 Forecasting the COVID-19 pandemic through hybridization of Machine Intelligent Algorithms - p. 314 24. Chapter 31 Suppression of noise signals from computed tomography and ultrasound medical images and performance evaluation - p. 324 25. Chapter 32 Prediction of nonalcoholic fatty liver disease using machine learning - p. 340 26. Chapter 33 Performance evaluation of diabetes with machine learning algorithms - p. 352 27. Chapter 34 Various segmentation techniques for medical images and the role of IoT - p. 362 28. Chapter 35 Augmented mass detection of breast cancer in mammogram images using deep intelligent neural network model - p. 382 29. Chapter 36 CNC machines in production and manufacturing of medical devices - p. 394 30. Chapter 37 Analysis and prediction of cardiomyopathy using artificial intelligence - p. 404 31. Chapter 38 A preemptive approach to polycystic ovary syndrome diagnosis using machine learning - p. 414 32. Chapter 39 Mapping the landscape of human activity recognition techniques in health monitoring for chronic disease management - p. 424 33. Chapter 40 Analysis and organization of mycological skin contaminations by means of medicinal imagery - p. 434 34. Chapter 41 A sensitive biosensor for the detection of blood components using 2D photonic crystals - p. 440 35. Chapter 42 Machine learning assisted EEG signal classification for automated diagnosis of mental stress - p. 448 36. Chapter 43 CNN based deep learning model for skin cancer detection using dermatoscopic images - p. 456 37. Chapter 44 Bioelectrical impedance analysis body composition estimation of fat mass percentage in people with spinal cord injury - p. 466 38. Chapter 45 Advanced EEG signal processing and feature extraction concepts - p. 474 39. Chapter 46 Fractal analysis on biomedical signal - p. 486 40. Chapter 47 Detection of metastasis osteosarcoma using deep fuzzy gradient recurrent convolutional neural network - p. 492 41. Chapter 48 Deep learning based fatigue detection using functional connectivity - p. 504 42. Chapter 49 Brain tumor diagnosis using image classifier - p. 514 43. Chapter 50 ISL recognition system in real time using TensorFlow API - p. 526 44. Chapter 51 Exploring the exciting potential and challenges of brain computer interfaces - p. 542 45. Chapter 52 Transmission dynamics of COVID-19 virus disease - p. 552 46. Chapter 53 Design of high voltage biphasic pulse generation circuit with 3-level isolation suitable for AED applications - p. 568 47. Chapter 54 A novel scheme of brain tumor detection from MRIs using K-means segmentation and histogram analysis - p. 578 48. Chapter 55 Analyzing post COVID-19 effects on self-consciousness and awareness toward health: A neuroscience framework - p. 588 49. Chapter 56 Crowdsourcing and artificial intelligence based modeling framework for effective Public Healthcare Informatics and Smart eHealth System - p. 598 50. Index - p. 609
Artificial Intelligence in Biomedical and Modern Healthcare Informatics provides a deeper understanding of the current trends in AI and machine learning within healthcare diagnosis, its practical approach in healthcare, and gives insight into different wearable sensors and its device module to help doctors and their patients in enhanced healthcare system. The primary goal of this book is to detect difficulties and their solutions to medical practitioners for the early detection and prediction of any disease. The 56 chapters in the volume provide beginners and experts in the medical science field with general pictures and detailed descriptions of imaging and signal processing principles and clinical applications. With forefront applications and up-to-date analytical methods, this book captures the interests of colleagues in the medical imaging research field and is a valuable resource for healthcare professionals who wish to understand the principles and applications of signal and image processing and its related technologies in healthcare.
1. Chapter 8 Electroencephalography (EEG) in epilepsy care: An introduction - p. 78 2. Chapter 9 A review on brain–computer interface and its applications - p. 84 3. Chapter 10 Recent trends in metabolomics, machine learning and artificial intelligence - p. 92 4. Chapter 11 A comprehensive review on state-of-the-art imagined speech decoding techniques using electroencephalography - p. 102 5. Chapter 12 Parkinson's disease diagnosis, treatment, and future scope: An epilogue - p. 128 6. Chapter 13 Recent advances in removal of artifacts from EEG signal records - p. 136 7. Chapter 14 Computer-aided diagnosis in healthcare: Case study on lung cancer diagnosis - p. 146 8. Chapter 15 AI and its role in predictive preclinical models for drug efficacy testing - p. 158 9. Chapter 16 Machine learning–based solutions for brain tumor detection: Comparative study and limitations - p. 166 10. Chapter 17 Indoor and home-based poststroke rehabilitation techniques—A systemic review - p. 176 11. Chapter 18 A comprehensive study on implementable antennas for medical applications - p. 186 12. Chapter 19 Deep learning for bone age assessment: Current status and future prospects - p. 194 13. Chapter 20 Emerging applications of artificial intelligence in analyzing EEG signals for the healthcare sector - p. 204 14. Chapter 21 Epilepsy detection system using CWT and deep CNN - p. 212 15. Chapter 22 Isolated Indian Sign Language Recognition with multihead attention transformer based network and MediaPipe's landmarks - p. 224 16. Chapter 23 Diagnosis of Parkinson's disease based on biological and imaging-derived features using machine learning and deep learning - p. 236 17. Chapter 24 Braintumor and feature detection from MRI and CT scan using artificial intelligence - p. 246 18. Chapter 25 Neuromodulation via brain stimulation: A promising therapeutic perspective for Alzheimer's disease - p. 258 19. Chapter 26 A biosensor for the detection of viruses using one-dimensional photonic crystals - p. 268 20. Chapter 27 Artificial intelligence–based seizure detection systems in electroencephalography: Transforming healthcare for accurate diagnosis and treatment - p. 278 21. Chapter 28 Artificial intelligence and image enhancement–based methodologies used for detection of tumor in MRIs of human brain - p. 290 22. Chapter 29 Machine learning-based workload identification using functional near-infrared spectroscopy (fNIRS) data - p. 300 23. Chapter 30 Forecasting the COVID-19 pandemic through hybridization of Machine Intelligent Algorithms - p. 314 24. Chapter 31 Suppression of noise signals from computed tomography and ultrasound medical images and performance evaluation - p. 324 25. Chapter 32 Prediction of nonalcoholic fatty liver disease using machine learning - p. 340 26. Chapter 33 Performance evaluation of diabetes with machine learning algorithms - p. 352 27. Chapter 34 Various segmentation techniques for medical images and the role of IoT - p. 362 28. Chapter 35 Augmented mass detection of breast cancer in mammogram images using deep intelligent neural network model - p. 382 29. Chapter 36 CNC machines in production and manufacturing of medical devices - p. 394 30. Chapter 37 Analysis and prediction of cardiomyopathy using artificial intelligence - p. 404 31. Chapter 38 A preemptive approach to polycystic ovary syndrome diagnosis using machine learning - p. 414 32. Chapter 39 Mapping the landscape of human activity recognition techniques in health monitoring for chronic disease management - p. 424 33. Chapter 40 Analysis and organization of mycological skin contaminations by means of medicinal imagery - p. 434 34. Chapter 41 A sensitive biosensor for the detection of blood components using 2D photonic crystals - p. 440 35. Chapter 42 Machine learning assisted EEG signal classification for automated diagnosis of mental stress - p. 448 36. Chapter 43 CNN based deep learning model for skin cancer detection using dermatoscopic images - p. 456 37. Chapter 44 Bioelectrical impedance analysis body composition estimation of fat mass percentage in people with spinal cord injury - p. 466 38. Chapter 45 Advanced EEG signal processing and feature extraction concepts - p. 474 39. Chapter 46 Fractal analysis on biomedical signal - p. 486 40. Chapter 47 Detection of metastasis osteosarcoma using deep fuzzy gradient recurrent convolutional neural network - p. 492 41. Chapter 48 Deep learning based fatigue detection using functional connectivity - p. 504 42. Chapter 49 Brain tumor diagnosis using image classifier - p. 514 43. Chapter 50 ISL recognition system in real time using TensorFlow API - p. 526 44. Chapter 51 Exploring the exciting potential and challenges of brain computer interfaces - p. 542 45. Chapter 52 Transmission dynamics of COVID-19 virus disease - p. 552 46. Chapter 53 Design of high voltage biphasic pulse generation circuit with 3-level isolation suitable for AED applications - p. 568 47. Chapter 54 A novel scheme of brain tumor detection from MRIs using K-means segmentation and histogram analysis - p. 578 48. Chapter 55 Analyzing post COVID-19 effects on self-consciousness and awareness toward health: A neuroscience framework - p. 588 49. Chapter 56 Crowdsourcing and artificial intelligence based modeling framework for effective Public Healthcare Informatics and Smart eHealth System - p. 598 50. Index - p. 609
