This book is designed to consider the recent advancements in hospitals to diagnose various diseases accurately using AI-supported detection procedures. This work examines recent AI-supported disease detection techniques from prominent researchers and clinicians working in the medical imaging processing domain. Within this book, the integration of various AI methods, such as soft computing, machine learning, deep learning, and other related works will be presented. Real clinical images utilizing AI are incorporated. The book also includes several chapters on machine learning, convoluted neural networks, segmentation, and deep learning-assisted two-class and multi-class classification.
Key Features:
- Implementation of machine-learning-assisted disease detection
- Implementation of CNN (Convolutional Neural Networks) based medical image segmentation and assessment
- Implementation of deep-learning-based medical data assessment
- Hybridizing machine learning and deep learning features to enhance detection accuracy
قائمة المحتويات
Preface
Acknowledgment
Editor Biography
List of Contributors
1 Health informatics system
2 Medical-imaging-supported disease diagnosis
3 Traditional and AI-based data enhancement
4 Computer-aided-scheme for automatic classification of brain MRI slices into normal/Alzheimer’s disease 5 Design of a system for melanoma diagnosis using image processing and hybrid optimization techniques 6 Evaluation of COVID-19 lesion from CT scan slices: a study using entropy-based thresholding and DRLS segmentation 7 Automated classification of brain tumors into LGG/HGGusing concatenated deep and handcrafted features 8 Detection of brain tumors in MRI slices using traditionalfeatures with AI scheme: a study
9 Framework to classify EEG signals into normal/schizophrenic classes with machine-learning scheme
10 Computerized classification of multichannel EEG signals intonormal/autistic classes using image-to-signal transformation
عن المؤلف
Navid Razmjooy is a Postdoc researcher at the industrial college of the Ankara Yıldırım Beyazıt Üniversitesi, Turkey. He is also a part-time assistant professor at the Islamic Azad University, Ardabil, Iran, and a part-time researcher at the Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATIC, India. His main areas of research are Renewable Energies, Machine Vision, Soft Computing, Data Mining, Evolutionary Algorithms, Interval Analysis, and System Control. Navid Razmjooy studied for his Ph.D. in the field of Electrical Engineering (Control and Automation) at Tafresh University, Iran (2018). He is a senior member of IEEE/USA and YRC in IAU/Iran. He has been ranked among the world’s top 2% of scientists in the world based on the Stanford University/Scopus database. He published more than 200 papers and 6 books in English and Persian in peer-reviewed journals and conferences and is now Editor and reviewer in several national and international journals and conferences which can be found at https://www.webofscience.com/wos/author/rid/D-4912-2012. More information can be found at: https://www.researchgate.net/profile/Navid_Razmjooy.
Venkatesan Rajinikanth, is a Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, SIMATIC, Chennai, India. He has published over 70 referred journal articles, largely related to medical imaging and image reconstruction through the utilization of machine learning. He has co-edited three books and co-authored one book, “Hybrid Image Processing Methods”, CRC Press 2020.
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