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

by Snehan Biswas, Amartya Mukherjee, Nilanjan Dey

Overview

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.

Product Details

ISBN-13:	9781040172360
Publisher:	CRC Press
Publication date:	12/02/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	198
File size:	28 MB
Note:	This product may take a few minutes to download.

About the Author

Snehan Biswas, is a Senior System Analyst in the department of Machine Learning and IoT, IEMA Research & Development Private Limited, India. He is a graduate in Electronics & Communication Engineering from University of Engineering and Management, Kolkata, India. His research interest includes Medical Image Processing, Machine Learning, Deep Learning, DevOps, Edge and Cloud computing. He has written several research articles in the field of Deep Learning, Machine learning, and Cloud Computing.

Amartya Mukherjee, is Head of the Department in the Department of CSE(AIML), Institute of Engineering & Management, Kolkata, India. He is currently doing his research at the Maulana Abul Kalam Azad University of Technology, West Bengal, India. He holds a master's degree in Computer Science and Engineering from the NIT, Durgapur, West Bengal, India. His research interest includes Machine Learning, Deep Learning, IoT, Wireless communication, Sensor networks, healthcare. He has written many research articles and books in the domain of IoT, Machine learning, Bio medical systems, Sensor networks.

Nilanjan Dey, is an Associate Professor in Department of Computer Science & Engineering at Techno International New Town, New Town, Kolkata, India. He is a visiting fellow of the University of Reading, UK. He is a Visiting Professor at Wenzhou Medical University, China and Duy Tan University, Vietnam, He was an honorary Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012-2015). He was awarded his PhD. from Jadavpur Univeristy in 2015. He has authored/edited more than 45 books with several reputed publishers, and published more than 300 papers. His main research interests include Medical Imaging, Machine learning, Computer Aided Diagnosis, Data Mining etc. He is the Indian Ambassador of International Federation for Information Processing (IFIP) – Young ICT Group. Recently, he has been awarded as one among the top 10 most published academics in the field of Computer Science in India (2015-17).

Table of Contents

1. Introduction to Medical Data and Image Analysis 2. The Convolutional Neural Network 3. The Detection of COVID-19 Pneumonia Using Inception V3 and Custom Designed Bi-Modal Looping DCNN via Analysis of X-Ray Images 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 5. An Adaptive Profound Transfer Learning Strategy for Malaria Cell Parasite Classification and Detection 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 7. Super Resolution Generative Adversarial Neural Network (SR-GANN) with Bi-Modal Multi-Perceptron Layers for Medical X-Ray Images 8. Conclusion