Full 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.
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
