Applications of Artificial Intelligence in Medical Imaging

Applications of Artificial Intelligence in Medical Imaging

1st Edition - November 10, 2022

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  • Editor: Abdulhamit Subasi
  • Paperback ISBN: 9780443184505
  • eBook ISBN: 9780443184512

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Description

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.

Key Features

  • Discusses new deep learning algorithms for image analysis and how they are used for medical images
  • Provides several examples for each imaging technique, along with their application areas so that readers can rely on them as a clinical decision support system
  • Describes how new AI tools may contribute significantly to the successful enhancement of a single patient's clinical knowledge to improve treatment outcomes

Readership

Researchers, professionals, academicians, and graduate students on medical informatics and computer science

Table of Contents

  • 1. Introduction to artificial intelligence techniques for medical image analysis

    ABDULHAMIT SUBASI

    2. Lung cancer detection from histopathological lung tissue images using deep learning

    AAYUSH RAJPUT AND ABDULHAMIT SUBASI

    3. Magnetic resonance imagining-based automated brain tumor detection using deep learning techniques

    ABHRANTA PANIGRAHI AND ABDULHAMIT SUBASI

    4. Breast cancer detection from mammograms using artificial intelligence

    ABDULHAMIT SUBASI, AAYUSH DINESH KANDPAL, KOLLA ANANT RAJ AND ULAS BAGCI

    5. Breast tumor detection in ultrasound images using artificial intelligence

    OMKAR MODI AND ABDULHAMIT SUBASI

    6. Artificial intelligence-based skin cancer diagnosis

    ABDULHAMIT SUBASI AND SAQIB AHMED QURESHI

    7. Brain stroke detection from computed tomography images using deep learning algorithms

    AYKUT DIKER, ABDULLAH ELEN AND ABDULHAMIT SUBASI

    8. A deep learning approach for COVID-19 detection from computed tomography scans

    ASHUTOSH VARSHNEY AND ABDULHAMIT SUBASI

    9. Detection and classification of Diabetic Retinopathy Lesions using deep learning

    SIDDHESH SHELKE AND ABDULHAMIT SUBASI

    10. Automated detection of colon cancer using deep learning

    AAYUSH RAJPUT AND ABDULHAMIT SUBASI

    11. Brain hemorrhage detection using computed tomography images and deep learning

    ABDULLAH ELEN, AYKUT DIKER AND ABDULHAMIT SUBASI

    12. Artificial intelligence-based retinal disease classification using optical coherence tomography images

    SOHAN PATNAIK AND ABDULHAMIT SUBASI

    13. Diagnosis of breast cancer from histopathological images with deep learning architectures

    EMRAH HANCER AND ABDULHAMIT SUBASI

    14. Artificial intelligence based Alzheimer’s disease detection using deep feature extraction

    MANAV NITIN KAPADNIS, ABHIJIT BHATTACHARYYA AND ABDULHAMIT SUBASI

Product details

  • No. of pages: 380
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: November 10, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780443184505
  • eBook ISBN: 9780443184512

About the Editor

Abdulhamit Subasi

Abdulhamit Subasi
Prof. Dr. Abdulhamit Subasi is specialized in Machine Learning, Data mining and Biomedical Signal Processing. Concerning application of machine learning to different fields, he wrote seven book chapters and more than 150 published journal and conference papers. He is also author of the book, “Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques”. He worked at many institutions as an academician and Georgia Institute of Technology, Georgia, USA, as a researcher. He has been awarded with the Queen Effat Award for Excellence in Research, May 2018. Since 2015, he has been working as a Professor of Information Systems at Effat University, Jeddah, Saudi Arabia. He has worked on several projects related to biomedical signal processing and data analysis.

Affiliations and Expertise

Professor of Information Systems at Effat University, Jeddah, Saudi Arabia

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