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Machine Learning and Deep Learning Techniques for Medical Image Recognition (eBook)

Artikelnummer: 978-1-00-380570-0
Einband: Adobe Digital Editions
Verfügbarkeit: Download, sofort verfügbar (Link per E-Mail)
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1 Medical Image Detection and Recognition Using Machine Learning and Deep Learning 2 Multiple Lung Disease Prediction Using X-Ray Images Based on Deep Convolutional Neural Networks 3 Analysis of Machine Learning and Deep Learning in Health Informatics, and Their Application 4 Automated Acute Lymphoblastic Leukemia Detection Using Blood Smear Image Analysis 5 Smart Digital Healthcare Solutions Using Medical Imaging and Advanced AI Techniques 6 Efficient and Fast Lung Disease Predictor Model 7 Artificial Intelligence Used to Recognize Fetal Planes Based on Ultrasound Scans during Pregnancy 8 Artificial Intelligence Techniques for Cancer Detection from Medical Images 9 Handling Segmentation and Classification Problems in Deep Learning for Identification of Interstitial Lung Disease 10 Computer Vision Approaches in Radiograph Image Analysis: A Targeted Review of Current Progress, Challenges, and Future Perspective 11 Deep Learning Methods for Brain Tumor Segmentation 12 Face Mask Detection and Temperature Scanning for the COVID-19 Surveillance System Based on Deep Learning Models 13 Diabetic Disease Prediction Using Machine Learning Models and Algorithms for Early Classification and Diagnosis Assessment 14 Defeating Alzheimer's: AI Perspective from Diagnostics to Prognostics: Literature Summary

1 Medical Image Detection and Recognition Using Machine Learning and Deep Learning 2 Multiple Lung Disease Prediction Using X-Ray Images Based on Deep Convolutional Neural Networks 3 Analysis of Machine Learning and Deep Learning in Health Informatics, and Their Application 4 Automated Acute Lymphoblastic Leukemia Detection Using Blood Smear Image Analysis 5 Smart Digital Healthcare Solutions Using Medical Imaging and Advanced AI Techniques 6 Efficient and Fast Lung Disease Predictor Model 7 Artificial Intelligence Used to Recognize Fetal Planes Based on Ultrasound Scans during Pregnancy 8 Artificial Intelligence Techniques for Cancer Detection from Medical Images 9 Handling Segmentation and Classification Problems in Deep Learning for Identification of Interstitial Lung Disease 10 Computer Vision Approaches in Radiograph Image Analysis: A Targeted Review of Current Progress, Challenges, and Future Perspective 11 Deep Learning Methods for Brain Tumor Segmentation 12 Face Mask Detection and Temperature Scanning for the COVID-19 Surveillance System Based on Deep Learning Models 13 Diabetic Disease Prediction Using Machine Learning Models and Algorithms for Early Classification and Diagnosis Assessment 14 Defeating Alzheimer's: AI Perspective from Diagnostics to Prognostics: Literature Summary

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VerlagTaylor & Francis Ebooks
EinbandAdobe Digital Editions
Erscheinungsjahr2023
Seitenangabe270 S.
AusgabekennzeichenEnglisch
Abbildungen119 schwarz-weiße Abbildungen, 46 schwarz-weiße Fotos, 73 schwarz-weiße Zeichnungen, 62 schwarz-weiße Tabellen
Auflage23001 A. 1. Auflage
PlattformEPUB
AutorSoufiene, Ben Othman (Hrsg.) / Chakraborty, Chinmay (Hrsg.)

Über den Autor Ben Othman (Hrsg.) Soufiene

Ben Othman Soufiene is an assistant professor of computer science at the University of Gabes, Tunisia. He earned a PhD in computer science at Manouba University in 2016 for his dissertation on "Secure Data Aggregation in Wireless Sensor Networks." He earned an MS degree at Monastir University in 2012. His research interests include the Internet of Medical Things, wireless body sensor networks, wireless networks, artificial intelligence, machine learning, and big data. He has coauthored more than 110 research articles with a Google H-index of 20.Saurav Mallik (Member, IEEE) earned a PhD at Jadavpur University, Kolkata, India, in 2017. His postgraduate studies were conducted with the Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. He is a research scientist at the University of Arizona, USA. He previously held a postdoctoral fellow with Environmental Epigenetics, Harvard T. H. Chan School of Public Health, University of Texas Health Science Center, Houston, and with the Miller School of Medicine, University of Miami, USA. He has coauthored more than 150 research articles with a Google H-index of 20. His research interests include computational biology, bioinformatics, data mining, biostatistics, and pattern recognition.Abdulatif Alabdulatif is an assistant professor at the School of Computer Science and Information Technology, Qassim University, Saudi Arabia. He earned a PhD in computer science at RMIT University, Australia, in 2018. He earned a BSc in computer science at Qassim University, Saudi Arabia, in 2008, and an MSc in computer science at RMIT University, Australia, in 2013. He has published more than 70 academic papers in prominent journals. His research interests include applied cryptography, cloud computing, and e-health.

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