Deep Learning in Biomedical Signal and Medical Imaging
portes grátis
Deep Learning in Biomedical Signal and Medical Imaging
Gochhayat, Sarada Prasad; Singh, Ngangbam Herojit; Kose, Utku
Taylor & Francis Ltd
09/2024
256
Dura
9781032622606
15 a 20 dias
Descrição não disponível.
1. Detection of Diabetic Retinopathy from retinal fundus images by using CNN Model ResNet-50.
2. DNASNet-RF: Automated Deep NAS-network with Random Forest for Classifying and Detecting Multi-class Brain Tumor. 3. Deep CNNs in image-guided diagnosis of breast and skin cancers. 4. Robust Learning Principle Design to Detect Diabetic Retinopathy Disease in Early Stages with Skilled Feature Extraction Policy. 5. Liver Tumour Detection using Machine Learning Techniques: A Systematic Review. 6. Deep Learning in Photoacoustic Tomographic Image Reconstruction. 7. Design and Development of Computer Aided Diagnosis to Detect Lung Cancer Disease by Using Intelligent Deep Learning Principle. 8. Novel Methodology to Predict and Classify Liver Diseases Based on Hybrid Deep Learning Strategy. 9. Improvements in Analysing Biomedical Signals and Medical Images Using Deep Learning. 10. A Survey on Lung Cancer Diagnosis Using Deep Learning Techniques. 11. Content-Based Medical Image Retrieval using CNN Feature Extraction and Hashing Dimensionality Reduction. 12. Experimental Evaluation of Deep Learning-Assisted Brain Tumor Identification with Advanced Classification Methodology. 13. Study of Biomedical Segmentation Based On Recent Techniques and Deep Learning. 14. Deep CNN in Healthcare. 15. An Improved Multi-Class Breast Cancer Classification and Abnormality Detection Based On Modified Deep Learning Neural Network Principles
2. DNASNet-RF: Automated Deep NAS-network with Random Forest for Classifying and Detecting Multi-class Brain Tumor. 3. Deep CNNs in image-guided diagnosis of breast and skin cancers. 4. Robust Learning Principle Design to Detect Diabetic Retinopathy Disease in Early Stages with Skilled Feature Extraction Policy. 5. Liver Tumour Detection using Machine Learning Techniques: A Systematic Review. 6. Deep Learning in Photoacoustic Tomographic Image Reconstruction. 7. Design and Development of Computer Aided Diagnosis to Detect Lung Cancer Disease by Using Intelligent Deep Learning Principle. 8. Novel Methodology to Predict and Classify Liver Diseases Based on Hybrid Deep Learning Strategy. 9. Improvements in Analysing Biomedical Signals and Medical Images Using Deep Learning. 10. A Survey on Lung Cancer Diagnosis Using Deep Learning Techniques. 11. Content-Based Medical Image Retrieval using CNN Feature Extraction and Hashing Dimensionality Reduction. 12. Experimental Evaluation of Deep Learning-Assisted Brain Tumor Identification with Advanced Classification Methodology. 13. Study of Biomedical Segmentation Based On Recent Techniques and Deep Learning. 14. Deep CNN in Healthcare. 15. An Improved Multi-Class Breast Cancer Classification and Abnormality Detection Based On Modified Deep Learning Neural Network Principles
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Biomedical Image Analysis;Deep CNN;Classification of Diseases;Biomedical Signal Analysis;Signal Processing;Image Segmentation;Computer Vision;Biomedicine;Medical Diagnostics
1. Detection of Diabetic Retinopathy from retinal fundus images by using CNN Model ResNet-50.
2. DNASNet-RF: Automated Deep NAS-network with Random Forest for Classifying and Detecting Multi-class Brain Tumor. 3. Deep CNNs in image-guided diagnosis of breast and skin cancers. 4. Robust Learning Principle Design to Detect Diabetic Retinopathy Disease in Early Stages with Skilled Feature Extraction Policy. 5. Liver Tumour Detection using Machine Learning Techniques: A Systematic Review. 6. Deep Learning in Photoacoustic Tomographic Image Reconstruction. 7. Design and Development of Computer Aided Diagnosis to Detect Lung Cancer Disease by Using Intelligent Deep Learning Principle. 8. Novel Methodology to Predict and Classify Liver Diseases Based on Hybrid Deep Learning Strategy. 9. Improvements in Analysing Biomedical Signals and Medical Images Using Deep Learning. 10. A Survey on Lung Cancer Diagnosis Using Deep Learning Techniques. 11. Content-Based Medical Image Retrieval using CNN Feature Extraction and Hashing Dimensionality Reduction. 12. Experimental Evaluation of Deep Learning-Assisted Brain Tumor Identification with Advanced Classification Methodology. 13. Study of Biomedical Segmentation Based On Recent Techniques and Deep Learning. 14. Deep CNN in Healthcare. 15. An Improved Multi-Class Breast Cancer Classification and Abnormality Detection Based On Modified Deep Learning Neural Network Principles
2. DNASNet-RF: Automated Deep NAS-network with Random Forest for Classifying and Detecting Multi-class Brain Tumor. 3. Deep CNNs in image-guided diagnosis of breast and skin cancers. 4. Robust Learning Principle Design to Detect Diabetic Retinopathy Disease in Early Stages with Skilled Feature Extraction Policy. 5. Liver Tumour Detection using Machine Learning Techniques: A Systematic Review. 6. Deep Learning in Photoacoustic Tomographic Image Reconstruction. 7. Design and Development of Computer Aided Diagnosis to Detect Lung Cancer Disease by Using Intelligent Deep Learning Principle. 8. Novel Methodology to Predict and Classify Liver Diseases Based on Hybrid Deep Learning Strategy. 9. Improvements in Analysing Biomedical Signals and Medical Images Using Deep Learning. 10. A Survey on Lung Cancer Diagnosis Using Deep Learning Techniques. 11. Content-Based Medical Image Retrieval using CNN Feature Extraction and Hashing Dimensionality Reduction. 12. Experimental Evaluation of Deep Learning-Assisted Brain Tumor Identification with Advanced Classification Methodology. 13. Study of Biomedical Segmentation Based On Recent Techniques and Deep Learning. 14. Deep CNN in Healthcare. 15. An Improved Multi-Class Breast Cancer Classification and Abnormality Detection Based On Modified Deep Learning Neural Network Principles
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
