A Beginner’s Guide to Medical Application Development with Deep Convolutional Neural Networks
by: Snehan Biswas (Author),Amartya Mukherjee(Author),Nilanjan Dey(Author)&0more
Publisher: CRC Press
Edition: 1st
Publication Date: 2024/12/2
Language: English
Print Length: 184 pages
ISBN-10: 1032589272
ISBN-13: 9781032589275
Book Description
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.
About the Author
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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