A Beginner's Guide to Medical Application Development with Deep Convolutional Neural Networks
eBook - ePub

A Beginner's Guide to Medical Application Development with Deep Convolutional Neural Networks

  1. 192 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

A Beginner's Guide to Medical Application Development with Deep Convolutional Neural Networks

About this book

This book serves as a source of introductory material and reference for medical application development and related technologies by providing the detailed implementation of cutting-edge deep learning methodologies. It targets cloud-based advanced medical application developments using open-source Python-based deep learning libraries. It includes code snippets and sophisticated convolutional neural networks to tackle real-world problems in medical image analysis and beyond.

Features:

  • Provides programming guidance for creation of sophisticated and reliable neural networks for image processing.

  • Incorporates the comparative study on GAN, stable diffusion, and its application on medical image data augmentation.

  • Focuses on solving real-world medical imaging problems.

  • Discusses advanced concepts of deep learning along with the latest technology such as GPT, stable diffusion, and ViT.

  • Develops applicable knowledge of deep learning using Python programming, followed by code snippets and OOP concepts.

This book is aimed at graduate students and researchers in medical data analytics, medical image analysis, signal processing, and deep learning.

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Yes, you can access A Beginner's Guide to Medical Application Development with Deep Convolutional Neural Networks by Snehan Biswas,Amartya Mukherjee,Nilanjan Dey in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Biomedical Science. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half-Title Page
  3. Title Page
  4. Copyright Page
  5. Table of Contents
  6. Preface
  7. Acknowledgments
  8. About the Authors
  9. 1 Introduction to Medical Data and Image Analysis
  10. 2 The Convolutional Neural Network
  11. 3 The Detection of COVID-19 Pneumonia Using Inception V3 and Custom Designed Bi-Modal Looping DCNN via Analysis of X-Ray Images
  12. 4 Detection of Pneumonia from a Small-Scale Dataset of X-Ray Images of Lungs by Using a Compound Batch-Normalizing Convolutional Neural Feature Extracting Random Forest Classifier
  13. 5 An Adaptive Profound Transfer Learning Strategy for Malaria Cell Parasite Classification and Detection
  14. 6 Implementation of a Deep Convolutional Auto-Encoding Image-Reconstruction Network (DCARN) to Visualize Distinct Categories of COVID-19 and Pneumonia X-Ray Image Features
  15. 7 Super Resolution Generative Adversarial Neural Network (SR-GANN) with Bi-Modal Multi-Perceptron Layers for Medical X-Ray Images
  16. 8 Conclusion
  17. Index