Machine Learning and Deep Learning Techniques for Medical Science
portes grátis
Machine Learning and Deep Learning Techniques for Medical Science
Ngoc, Le Anh; Devi, K. Gayathri; Balasubramanian, Kishore
Taylor & Francis Ltd
05/2022
398
Dura
Inglês
9781032104201
15 a 20 dias
920
Descrição não disponível.
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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Neuroscience;AI Prediction tools;Functional magnetic resonance imaging (FMRI);Image Segmentation;Bio-inspired Computing Based Deep Neural Networks;DCNN;IoT -Intelligent Systems for Medical Diagnosis;Convolutional Layer;protein profiles;Dl Algorithm;enzyme;DNNs;metabolite;CNN Model;on lipid;DNN Model;Pattern Recognition;Deep Learning Model;Multiple Imaging Modalities;Convolution Layers;Deep Learning architectures;Supervised Machine Learning;Machine Learning;Pooling Layer;SVM;Fundus Images;Neural Networks;Multi-layer Perceptron;RNN;Ml Technique;Multilayer Perceptron;Optic Cup;Hidden Layer;Scatter Plot;RF;Input Image;DBM;Image Classification
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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Neuroscience;AI Prediction tools;Functional magnetic resonance imaging (FMRI);Image Segmentation;Bio-inspired Computing Based Deep Neural Networks;DCNN;IoT -Intelligent Systems for Medical Diagnosis;Convolutional Layer;protein profiles;Dl Algorithm;enzyme;DNNs;metabolite;CNN Model;on lipid;DNN Model;Pattern Recognition;Deep Learning Model;Multiple Imaging Modalities;Convolution Layers;Deep Learning architectures;Supervised Machine Learning;Machine Learning;Pooling Layer;SVM;Fundus Images;Neural Networks;Multi-layer Perceptron;RNN;Ml Technique;Multilayer Perceptron;Optic Cup;Hidden Layer;Scatter Plot;RF;Input Image;DBM;Image Classification
