Full Description
Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards.
The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community.
The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.
Contents
1. A novel stacked model ensemble for improved Tuberculosis (TB) detection in chest radiographs. 2. The Role of Artificial Intelligence (AI) in Medical Imaging: General Radiologic and Urologic Applications. 3. Early Detection of Epileptic Seizures based on scalp EEG signals. 4. Fractal analysis in histology classification of non-small cell lung cancer. 5. Multi feature-based classification of osteoarthritis in knee joint x ray images. 6. Detection and classification of non-proliferative Diabetic Retinopathy Lesions. 7. Segmentation and analysis of CT images for bone fracture detection and labeling. 8. 3D imaging in biomedical applications: a systematic review. 9. Evolution of Digital sliding of pathology in medical imaging. 10. Pathological medical image segmentation: a quick review based on parametric techniques.
