Applications of Artificial Intelligence in Medical Imaging provides the description of various biomedical image analysis in disease detection using AI that can be used to incorporate knowledge obtained from different medical imaging devices such as CT, X-ray, PET and ultrasound. The book discusses the use of AI for detection of several cancer types, including brain tumor, breast, pancreatic, rectal, lung colon, and skin. In addition, it explains how AI and deep learning techniques can be used to diagnose Alzheimer's, Parkinson's, COVID-19 and mental conditions.
This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about AI and its impact in medical/biomedical image analysis.
1. Chapter 1 : Introduction to artificial intelligence techniques for medical image analysis - p. Page xv 2. Chapter 2 : Lung cancer detection from histopathological lung tissue images using deep learning - p. Page 48 3. Chapter 3 : Magnetic resonance imagining-based automated brain tumor detection using deep learning techniques - p. Page 72 4. Chapter 4 : Breast cancer detection from mammograms using artificial intelligence - p. Page 106 5. Chapter 5 : Breast tumor detection in ultrasound images using artificial intelligence - p. Page 135 6. Chapter 6 : Artificial intelligence-based skin cancer diagnosis - p. Page 180 7. Chapter 7 : Brain stroke detection from computed tomography images using deep learning algorithms - p. Page 203 8. Chapter 8 : A deep learning approach for COVID-19 detection from computed tomography scans - p. Page 221 9. Chapter 9 : Detection and classification of Diabetic Retinopathy Lesions using deep learning - p. Page 238 10. Chapter 10 : Automated detection of colon cancer using deep learning - p. Page 262 11. Chapter 11 : Brain hemorrhage detection using computed tomography images and deep learning - p. Page 280 12. Chapter 12 : Artificial intelligence-based retinal disease classification using optical coherence tomography images - p. Page 300 13. Chapter 13 : Diagnosis of breast cancer from histopathological images with deep learning architectures - p. Page 319 14. Chapter 14 : Artificial intelligence based Alzheimer’s disease detection using deep feature extraction - p. Page 332 15. Index - p. Page 352
Applications of Artificial Intelligence in Medical Imaging provides the description of various biomedical image analysis in disease detection using AI that can be used to incorporate knowledge obtained from different medical imaging devices such as CT, X-ray, PET and ultrasound. The book discusses the use of AI for detection of several cancer types, including brain tumor, breast, pancreatic, rectal, lung colon, and skin. In addition, it explains how AI and deep learning techniques can be used to diagnose Alzheimer's, Parkinson's, COVID-19 and mental conditions.
This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about AI and its impact in medical/biomedical image analysis.
1. Chapter 1 : Introduction to artificial intelligence techniques for medical image analysis - p. Page xv 2. Chapter 2 : Lung cancer detection from histopathological lung tissue images using deep learning - p. Page 48 3. Chapter 3 : Magnetic resonance imagining-based automated brain tumor detection using deep learning techniques - p. Page 72 4. Chapter 4 : Breast cancer detection from mammograms using artificial intelligence - p. Page 106 5. Chapter 5 : Breast tumor detection in ultrasound images using artificial intelligence - p. Page 135 6. Chapter 6 : Artificial intelligence-based skin cancer diagnosis - p. Page 180 7. Chapter 7 : Brain stroke detection from computed tomography images using deep learning algorithms - p. Page 203 8. Chapter 8 : A deep learning approach for COVID-19 detection from computed tomography scans - p. Page 221 9. Chapter 9 : Detection and classification of Diabetic Retinopathy Lesions using deep learning - p. Page 238 10. Chapter 10 : Automated detection of colon cancer using deep learning - p. Page 262 11. Chapter 11 : Brain hemorrhage detection using computed tomography images and deep learning - p. Page 280 12. Chapter 12 : Artificial intelligence-based retinal disease classification using optical coherence tomography images - p. Page 300 13. Chapter 13 : Diagnosis of breast cancer from histopathological images with deep learning architectures - p. Page 319 14. Chapter 14 : Artificial intelligence based Alzheimer’s disease detection using deep feature extraction - p. Page 332 15. Index - p. Page 352
