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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
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