The main purpose of the presented book is to collect the recent AI-supported disease detection techniques from various researchers working in the medical image processing domain. In this book, the integration of various AI methods, such as soft computing, machine learning, deep learning, and other related works will be presented. This book is aimed to collect novel contributions from the various authors working in image assisted disease diagnosis domain. Further, the editors are also contributing few chapters in the fields, such as machine-learning, CNN segmentation and deep-learning assisted two-class and multi-class classification. The book can be included but not limited to:
- Medical Image pre-processing and post-processing using AI
- Development of Machine Learning System to examine medical data
- Handcrafted feature extraction procedures, traditional feature selection, heuristic algorithm-based dominant feature selection, two-class and multi-class classifier implementation, and validation, etc)
- Implementation of Convolutional Neural Network (CNN) based Segmentation
- Segmentation with U-Net, SegNet, VGG-UNet, VGG-SegNet etc.
- Implementation of customized CNN based encoder-decoder based medical image segmentation
- Implementation of Pre-trained deep-learning schemes to detect and classify the medical images
- Implementation of customized deep-learning schemes for disease detection and classification
- Implementation of AI-supported Internet of Medical Things (IoMT) for patient monitoring
- Implementation of AI-supported body sensor network for patient monitoring
- Implementation of machine-learning assisted disease detection
- Implementation of CNN 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.
- Considering the possible number of real clinical images to confirm the clinical significance of the AI-supported diagnosis