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Synopsis
Deep Learning and Machine Learning Frameworks for Cervical Cancer Screening presents a comprehensive exploration of computational intelligence techniques applied to digital pathology, automated cytology, and cervical cancer diagnostic systems. As clinical oncology increasingly adopts computer-assisted diagnostic tools, designing resilient algorithm pipelines to evaluate Papanicolaou smears, colposcopy imagery, and histopathological tissue samples has become a critical engineering discipline. This monograph details the mathematical principles, model architectures, and data processing strategies required to build reliable machine learning models for early detection and risk stratification.
The text examines convolutional neural network architectures, transfer learning strategies, feature extraction routines, and hybrid classification algorithms optimized for complex cellular structures. It addresses fundamental technical challenges including class imbalance in clinical datasets, slide artifact filtering, multi-scale image segmentation, and model interpretability. Designed for biomedical engineers, data scientists, and medical imaging software developers, this volume provides rigorous insights into establishing automated screening systems that enhance diagnostic accuracy and operational efficiency in modern healthcare settings.
2. Short Description3. Keywords4. BISAC SubjectsCOMPUTERS / Data Science / Data Analytics
5. Thema SubjectsMKD Medical equipment & techniques / Biomedical engineering
UYQ Artificial intelligence & machine learning
MMF Radiology
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Deep Learning and Machine Learning Frameworks for Cervical Cancer Screening (Paperback)
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Paperback. Etat : new. Paperback. Deep Learning and Machine Learning Frameworks for Cervical Cancer Screening presents a comprehensive exploration of computational intelligence techniques applied to digital pathology, automated cytology, and cervical cancer diagnostic systems. As clinical oncology increasingly adopts computer-assisted diagnostic tools, designing resilient algorithm pipelines to evaluate Papanicolaou smears, colposcopy imagery, and histopathological tissue samples has become a critical engineering discipline. This monograph details the mathematical principles, model architectures, and data processing strategies required to build reliable machine learning models for early detection and risk stratification.The text examines convolutional neural network architectures, transfer learning strategies, feature extraction routines, and hybrid classification algorithms optimized for complex cellular structures. It addresses fundamental technical challenges including class imbalance in clinical datasets, slide artifact filtering, multi-scale image segmentation, and model interpretability. Designed for biomedical engineers, data scientists, and medical imaging software developers, this volume provides rigorous insights into establishing automated screening systems that enhance diagnostic accuracy and operational efficiency in modern healthcare settings.2. Short Description3. Keywords4. BISAC Subjects COMPUTERS / Data Science / Data Analytics5. Thema SubjectsMKD Medical equipment & techniques / Biomedical engineeringUYQ Artificial intelligence & machine learningMMF Radiology A technical guide detailing deep learning architectures, feature extraction models, and machine learning frameworks for automated cervical cancer screening. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798182718349
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Deep Learning and Machine Learning Frameworks for Cervical Cancer Screening (Paperback)
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Paperback. Etat : new. Paperback. Deep Learning and Machine Learning Frameworks for Cervical Cancer Screening presents a comprehensive exploration of computational intelligence techniques applied to digital pathology, automated cytology, and cervical cancer diagnostic systems. As clinical oncology increasingly adopts computer-assisted diagnostic tools, designing resilient algorithm pipelines to evaluate Papanicolaou smears, colposcopy imagery, and histopathological tissue samples has become a critical engineering discipline. This monograph details the mathematical principles, model architectures, and data processing strategies required to build reliable machine learning models for early detection and risk stratification.The text examines convolutional neural network architectures, transfer learning strategies, feature extraction routines, and hybrid classification algorithms optimized for complex cellular structures. It addresses fundamental technical challenges including class imbalance in clinical datasets, slide artifact filtering, multi-scale image segmentation, and model interpretability. Designed for biomedical engineers, data scientists, and medical imaging software developers, this volume provides rigorous insights into establishing automated screening systems that enhance diagnostic accuracy and operational efficiency in modern healthcare settings.2. Short Description3. Keywords4. BISAC Subjects COMPUTERS / Data Science / Data Analytics5. Thema SubjectsMKD Medical equipment & techniques / Biomedical engineeringUYQ Artificial intelligence & machine learningMMF Radiology A technical guide detailing deep learning architectures, feature extraction models, and machine learning frameworks for automated cervical cancer screening. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798182718349
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Deep Learning and Machine Learning Frameworks for Cervical Cancer Screening (Paperback)
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Paperback. Etat : new. Paperback. Deep Learning and Machine Learning Frameworks for Cervical Cancer Screening presents a comprehensive exploration of computational intelligence techniques applied to digital pathology, automated cytology, and cervical cancer diagnostic systems. As clinical oncology increasingly adopts computer-assisted diagnostic tools, designing resilient algorithm pipelines to evaluate Papanicolaou smears, colposcopy imagery, and histopathological tissue samples has become a critical engineering discipline. This monograph details the mathematical principles, model architectures, and data processing strategies required to build reliable machine learning models for early detection and risk stratification.The text examines convolutional neural network architectures, transfer learning strategies, feature extraction routines, and hybrid classification algorithms optimized for complex cellular structures. It addresses fundamental technical challenges including class imbalance in clinical datasets, slide artifact filtering, multi-scale image segmentation, and model interpretability. Designed for biomedical engineers, data scientists, and medical imaging software developers, this volume provides rigorous insights into establishing automated screening systems that enhance diagnostic accuracy and operational efficiency in modern healthcare settings.2. Short Description3. Keywords4. BISAC Subjects COMPUTERS / Data Science / Data Analytics5. Thema SubjectsMKD Medical equipment & techniques / Biomedical engineeringUYQ Artificial intelligence & machine learningMMF Radiology A technical guide detailing deep learning architectures, feature extraction models, and machine learning frameworks for automated cervical cancer screening. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9798182718349
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Deep Learning and Machine Learning Frameworks for Cervical Cancer Screening presents a comprehensive exploration of computational intelligence techniques applied to digital pathology, automated cytology, and cervical cancer diagnostic systems. As clinical oncology increasingly adopts computer-assisted diagnostic tools, designing resilient algorithm pipelines to evaluate Papanicolaou smears, colposcopy imagery, and histopathological tissue samples has become a critical engineering discipline. This monograph details the mathematical principles, model architectures, and data processing strategies required to build reliable machine learning models for early detection and risk stratification.The text examines convolutional neural network architectures, transfer learning strategies, feature extraction routines, and hybrid classification algorithms optimized for complex cellular structures. It addresses fundamental technical challenges including class imbalance in clinical datasets, slide artifact filtering, multi-scale image segmentation, and model interpretability. Designed for biomedical engineers, data scientists, and medical imaging software developers, this volume provides rigorous insights into establishing automated screening systems that enhance diagnostic accuracy and operational efficiency in modern healthcare settings.2. Short Description3. Keywords4. BISAC Subjects COMPUTERS / Data Science / Data Analytics5. Thema SubjectsMKD Medical equipment & techniques / Biomedical engineeringUYQ Artificial intelligence & machine learningMMF Radiology. N° de réf. du vendeur 9798182718349
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Deep Learning and Machine Learning Frameworks for Cervical Cancer Screening presents a comprehensive exploration of computational intelligence techniques applied to digital pathology, automated cytology, and cervical cancer diagnostic systems. As clinical oncology increasingly adopts computer-assisted diagnostic tools, designing resilient algorithm pipelines to evaluate Papanicolaou smears, colposcopy imagery, and histopathological tissue samples has become a critical engineering discipline. This monograph details the mathematical principles, model architectures, and data processing strategies required to build reliable machine learning models for early detection and risk stratification.The text examines convolutional neural network architectures, transfer learning strategies, feature extraction routines, and hybrid classification algorithms optimized for complex cellular structures. It addresses fundamental technical challenges including class imbalance in clinical datasets, slide artifact filtering, multi-scale image segmentation, and model interpretability. Designed for biomedical engineers, data scientists, and medical imaging software developers, this volume provides rigorous insights into establishing automated screening systems that enhance diagnostic accuracy and operational efficiency in modern healthcare settings.2. Short Description3. Keywords4. BISAC Subjects COMPUTERS / Data Science / Data Analytics5. Thema SubjectsMKD Medical equipment & techniques / Biomedical engineeringUYQ Artificial intelligence & machine learningMMF Radiology 118 pp. Englisch. N° de réf. du vendeur 9798182718349
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