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Название: A Beginner's Guide to Medical Application Development with Deep Convolutional Neural NetworksАвтор: Snehan Biswas, Amartya Mukherjee, Nilanjan Dey
Издательство: CRC Press
Год: 2025
Страниц: 199
Язык: английский
Формат: pdf (true), epub
Размер: 51.9 MB
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. In recent years, deep neural networks (DNNs) have emerged as a powerful tool for analyzing medical data. These networks are capable of learning complex patterns in data, and can be trained to identify subtle features that traditional methods may miss. With the ability to process large amounts of data quickly and accurately, DNNs offer a promising avenue for improving medical diagnosis and treatment. This book aims to provide a comprehensive introduction to the use of DNNs for medical data analysis. The book is intended for a wide audience, including healthcare professionals, data scientists, researchers, and students. It covers the basics of neural networks and Deep Learning, and provides a detailed overview of the various types of networks that can be used for medical data analysis. The book also includes case studies and practical examples of how DNNs have been used to diagnose and treat a range of medical conditions. Chapter 1 provides an overview of the basics of neural networks and Deep Learning. It covers the fundamentals of how neural networks work and how they can be used to learn from data. The chapter also explores different types of Deep Learning architectures and how they can be used for medical data analysis. Chapter 2 focuses on the details about a special type of neural network, known as the convolutional neural network (CNN), and how it can solve the problems of image analysis by learning different features of the training images. The chapter also provides an idea about some of the recent state-of-the-art models that have been developed by researchers all around the world. 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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