Machine learning and deep learning techniques for medical science
著者
書誌事項
Machine learning and deep learning techniques for medical science
（Artificial intelligence (AI) : elementary to advanced practices / series editors, Vijender Kumar Solanki, Zhongyu (Joan) Lu, and Valentina E. Balas）
CRC Press, 2022
- : hbk
大学図書館所蔵 全1件
注記
Includes bibliographical references and index
Summary: "This book presents the integration of machine learning and deep learning algorithms that can be applied in the healthcare sector to reduce the time needed by doctors, radiologists, and other medical professionals to analyze, predict, and diagnose conditions with accurate results"-- Provided by publisher
内容説明・目次
内容説明
Presents key aspects in the development and the implementation of machine learning and deep learning approaches towards developing prediction tools, models, and improving medical diagnosis
Discusses recent trends innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis
Examines deep learning theories, models, and tools to enhance health information systems
Explores ML and DL in relation to AI prediction tools discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities
目次
Chapter 1. A Comprehensive Study on MLP and CNN, and the Implementation of Multi-Class Image Classification using Deep CNN
Chapter 2. An Efficient Technique for Image Compression and Quality Retrieval in Diagnosis of Brain Tumour Hyper Spectral Image
Chapter 3. Classification of Breast Thermograms using a Multi-layer Perceptron with Back Propagation Learning
Chapter 4. Neural Networks for Medical Image Computing
Chapter 5. Recent Trends in Bio-Medical Waste, Challenges and Opportunities
Chapter 6. Teager-Kaiser Boost Clustered Segmentation of Retinal Fundus Images for Glaucoma Detection
Chapter 7. IoT-Based Deep Neural Network Approach for Heart Rate and SpO2 Prediction
Chapter 8. An Intelligent System for Diagnosis and Prediction of Breast Cancer Malignant Features using Machine Learning Algorithms
Chapter 9. Medical Image Classification with Artificial and Deep Convolutional Neural Networks: A Comparative Study
Chapter 10. Convolutional Neural Network for Classification of Skin Cancer Images
Chapter 11. Application of Artificial Intelligence in Medical Imaging
Chapter 12. Machine Learning Algorithms Used in Medical Field with a Case Study
Chapter 13. Dual Customized U-Net-based Based Automated Diagnosis of Glaucoma
Chapter 14. MuSCF-Net: Multi-scale, Multi-Channel Feature Network using Resnet-Based Attention Mechanism for Breast Histopathological Image Classification
Chapter 15. Artificial Intelligence is Revolutionizing Cancer Research
Chapter 16. Deep Learning to Diagnose Diseases and Security in 5G Healthcare InformaticsChapter 17. New Approaches in Machine-based Image Analysis for Medical Oncology
Chapter 18. Performance Analysis of Deep Convolutional Neural Networks for Diagnosing COVID-19: Data to Deployment
Chapter 19. Stacked Auto Encoder Deep Neural Network with Principal Components Analysis for Identification of Chronic Kidney Disease
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