AI Innovation in Medical Imaging Diagnostics, 1st Edition

  • Kalaivani Anbarasan
  • Published By:
  • ISBN-10: 1799830934
  • ISBN-13: 9781799830931
  • DDC: 616.07
  • 248 Pages | eBook
  • Original Copyright 2021 | Published/Released July 2021
  • This publication's content originally published in print form: 2021

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Recent advancements in the technology of medical imaging, such as CT and MRI scanners, are making it possible to create more detailed 3D and 4D images. These powerful images require vast amounts of digital data to help with the diagnosis of the patient. Artificial intelligence (AI) must play a vital role in supporting with the analysis of this medical imaging data, but it will only be viable as long as healthcare professionals and AI interact to embrace deep thinking platforms such as automation in the identification of diseases in patients. This title is an essential reference source that examines AI applications in medical imaging that can transform hospitals to become more efficient in the management of patient treatment plans through the production of faster imaging and the reduction of radiation dosages through the PET and SPECT imaging modalities. The book also explores how data clusters from these images can be translated into small data packages that can be accessed by healthcare departments to give a real-time insight into patient care and required interventions. Featuring research on topics such as assistive healthcare, cancer detection, and machine learning, this book is ideally designed for healthcare administrators, radiologists, data analysts, computer science professionals, medical imaging specialists, diagnosticians, medical professionals, researchers, and students.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Advances in Medical Technologies and Clinical Practice (AMTCP) Book Series.
Table of Contents.
Detailed Table of Contents.
1: Detection of Ocular Pathologies From Iris Images Using Blind De-Convolution and Fuzzy C-Means Clustering: Detection of Ocular Pathologies.
2: Machine Learning in Healthcare.
3: Detection of Tumor From Brain MRI Images Using Supervised and Unsupervised Methods.
4: Breast Cancer Diagnosis in Mammograms Using Wavelet Analysis, Haralick Descriptors, and Autoencoder.
5: Feature Selection Using Random Forest Algorithm to Diagnose Tuberculosis From Lung CT Images.
6: An Ensemble Feature Subset Selection for Women Breast Cancer Classification.
7: A Content-Based Approach to Medical Image Retrieval.
8: Correlation and Analysis of Overlapping Leukocytes in Blood Cell Images Using Intracellular Markers and Colocalization Operation.
9: Enchodroma Tumor Detection From MRI Images Using SVM Classifier.
10: An Approach to Cloud Computing for Medical Image Analysis.
11: Segmentation of Spine Tumour Using K-Means and Active Contour and Feature Extraction Using GLCM.
12: A Survey on Early Detection of Women’s Breast Cancer Using IoT.
Compilation of References.
About the Contributors.