標(biāo)題: Titlebook: Data-Driven Clinical Decision-Making Using Deep Learning in Imaging; M. F. Mridha,Nilanjan Dey Book 2024 The Editor(s) (if applicable) and [打印本頁] 作者: TUMOR 時間: 2025-3-21 17:10
書目名稱Data-Driven Clinical Decision-Making Using Deep Learning in Imaging影響因子(影響力)
書目名稱Data-Driven Clinical Decision-Making Using Deep Learning in Imaging影響因子(影響力)學(xué)科排名
書目名稱Data-Driven Clinical Decision-Making Using Deep Learning in Imaging網(wǎng)絡(luò)公開度
書目名稱Data-Driven Clinical Decision-Making Using Deep Learning in Imaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data-Driven Clinical Decision-Making Using Deep Learning in Imaging被引頻次
書目名稱Data-Driven Clinical Decision-Making Using Deep Learning in Imaging被引頻次學(xué)科排名
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書目名稱Data-Driven Clinical Decision-Making Using Deep Learning in Imaging年度引用學(xué)科排名
書目名稱Data-Driven Clinical Decision-Making Using Deep Learning in Imaging讀者反饋
書目名稱Data-Driven Clinical Decision-Making Using Deep Learning in Imaging讀者反饋學(xué)科排名
作者: GENUS 時間: 2025-3-21 22:29 作者: conceal 時間: 2025-3-22 00:31
,A Precise Cervical Cancer Classification in?the Early Stage Using Transfer Learning-Based Ensemble eatment, a principle applicable to all cancer variants. Although the Pap smear test stands as the benchmark for this type of cancer diagnosis, the accuracy of this diagnosis depends on the skill and attentiveness of the healthcare provider. Considerable efforts have been directed toward leveraging a作者: monochromatic 時間: 2025-3-22 04:40
,Unveiling Diagnostic Precision: Evaluating Machine Learning and?Deep Learning Approaches for?Pneumoion from large and complex medical image datasets. Currently, medical image datasets are increasing rapidly in size and complexity. Additionally, these algorithms are capable of processing and analyzing enormous amounts of data much more quickly and precisely than manual methods. However, it is chal作者: Gossamer 時間: 2025-3-22 10:34 作者: nutrition 時間: 2025-3-22 14:09
Privacy-Preserving Vision-Based Detection of Pox Diseases Using Federated Learning,tection is vital for effective disease management and prevention. Traditional diagnostic methods often rely on invasive procedures and may lack privacy safeguards. In response, this research leverages advanced image analysis and federated learning to introduce a privacy-preserving framework for pox 作者: nutrition 時間: 2025-3-22 18:11
,Unveiling the?Unique Dermatological Signatures of?Human Pox Diseases Through Deep Transfer Learningies, potentially leading to misdiagnosis and delayed treatment. Currently, doctors look at samples by hand or rely on confirmation tests that are not always easy to obtain, such as polymerase chain reaction (PCR) tests, which take a long time. A few studies have focused on individual disease classif作者: Inflated 時間: 2025-3-22 21:56
,Improved Classification of?Kidney Lesions in?CT Scans Using CNN with?Attention Layers: Achieving Hier pathological abnormalities. Precise identification and categorization of kidney abnormalities using medical imaging methods is essential for precise diagnosis and efficient treatment planning in nephrology. This paper introduces an innovative deep-learning method for precisely categorising CT kid作者: Externalize 時間: 2025-3-23 02:04 作者: 值得尊敬 時間: 2025-3-23 05:58 作者: Nefarious 時間: 2025-3-23 13:05 作者: Myofibrils 時間: 2025-3-23 17:26 作者: debris 時間: 2025-3-23 21:01 作者: Venules 時間: 2025-3-24 00:11 作者: 冷漠 時間: 2025-3-24 03:27 作者: Assignment 時間: 2025-3-24 07:00 作者: Contort 時間: 2025-3-24 11:22
Eisen(II)-hydrocarbonat Fe(HCO3)2,the gradient problem, resulting in an optimized and efficient training process. Our proposed model outperformed all existing models including the SOTA model, with an accuracy of 89.95%, precision of 91.42%, recall of 88.84%, F1 of 89.68%, and specificity of 95.98%.作者: N斯巴達(dá)人 時間: 2025-3-24 16:32 作者: 價值在貶值 時間: 2025-3-24 23:05
,Advancing Brain Tumour Detection: Transfer Learning-Based Approach Fused with?Squeeze-and-Excitatioenchmarked with the previous seven state-of-the-art (SOTA) models on the same dataset. Our proposed techniques obtained the best results for both the validation and testing datasets. On the validation data of the MRI brain tumour, we achieved the highest results, with an accuracy of 95.92%, precision of 95.89%, recall of 95.24% and AUC of 99.00%.作者: mastopexy 時間: 2025-3-25 02:22
Enhancing Breast Cancer Detection Systems: Augmenting Mammogram Images Using Generative Adversarialbuted to the labor-intensive curation and labeling of images, coupled with privacy concerns, serves as a driving force behind investigating GANs as a potential solution. This exploration aims to address the challenge of obtaining a more extensive and diverse dataset, essential for the robust training of breast cancer detection systems.作者: Bother 時間: 2025-3-25 06:47
,Incorporating Residual Connections into?a?Multi-channel CNN for?Lung Cancer Detection in?Digital Pathe gradient problem, resulting in an optimized and efficient training process. Our proposed model outperformed all existing models including the SOTA model, with an accuracy of 89.95%, precision of 91.42%, recall of 88.84%, F1 of 89.68%, and specificity of 95.98%.作者: 噱頭 時間: 2025-3-25 10:09
Book 2024thodologies, and applications, providing readers with a comprehensive understanding of the field‘s current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled d作者: CRUC 時間: 2025-3-25 11:44
2197-6503 l machine learning models.Brings together a global network o.This book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the作者: 一美元 時間: 2025-3-25 19:49
,Westindien und Mittelmeer 1871–74,stinct data distribution variations in each domain. This research delves into the effectiveness of transfer learning, specifically within the domain adaptation framework for medical imaging, addressing the challenges posed by varying data distributions across different medical domains. This paper us作者: 不能根除 時間: 2025-3-25 20:03
al decisions for patients and pathologists. Early diagnosis can help with prior treatment and reduce the mortality rate. In this research, we proposed a transfer learning (TL) approach fused with a squeeze-and-excitation (SE) attention mechanism to accurately diagnose brain tumours on a brain tumour作者: 豪華 時間: 2025-3-26 04:02 作者: Pulmonary-Veins 時間: 2025-3-26 07:09
Klaus Dieter Lorenzen,Wilfried Krokowskiion from large and complex medical image datasets. Currently, medical image datasets are increasing rapidly in size and complexity. Additionally, these algorithms are capable of processing and analyzing enormous amounts of data much more quickly and precisely than manual methods. However, it is chal作者: 能量守恒 時間: 2025-3-26 09:48
Klaus Dieter Lorenzen,Wilfried Krokowski cancers is crucial for effective treatment planning and patient management. Leukemia and myeloma (plasma cell cancer), one types of malignancy that can damage the white blood cells (WBC) within the bone marrow. White blood cell identification, counting, and segmentation are crucial steps in effecti作者: LUT 時間: 2025-3-26 15:19 作者: 使隔離 時間: 2025-3-26 17:01
Der beleidigte gesunde Menschenverstand,ies, potentially leading to misdiagnosis and delayed treatment. Currently, doctors look at samples by hand or rely on confirmation tests that are not always easy to obtain, such as polymerase chain reaction (PCR) tests, which take a long time. A few studies have focused on individual disease classif作者: 窒息 時間: 2025-3-27 00:21
https://doi.org/10.1007/978-3-322-99011-2er pathological abnormalities. Precise identification and categorization of kidney abnormalities using medical imaging methods is essential for precise diagnosis and efficient treatment planning in nephrology. This paper introduces an innovative deep-learning method for precisely categorising CT kid作者: cogitate 時間: 2025-3-27 02:54
Verlag Chemie, Weinheim/Bergstr.ccessible breast cancer datasets like CBIS-DDSM. The primary objective is to produce a more varied set of images to enrich the training data for breast cancer detection systems by training a neural network to learn the inherent characteristics of mammogram images and generate new images which did no作者: 親愛 時間: 2025-3-27 08:45 作者: MERIT 時間: 2025-3-27 12:16
,H?rtebestimmungen an Sondermaterialien, novel approach to breast cancer segmentation by proposing an attention-enhanced U-Net architecture. The primary objective is to elevate the precision and efficiency of the segmentation process. Within our proposed architecture, attention blocks are seamlessly integrated into the U-Net framework, em作者: 闖入 時間: 2025-3-27 17:03 作者: Flat-Feet 時間: 2025-3-27 19:07 作者: 眼界 時間: 2025-3-27 21:56 作者: neutralize 時間: 2025-3-28 05:04
978-981-97-3968-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: 馬賽克 時間: 2025-3-28 08:47 作者: 作嘔 時間: 2025-3-28 13:54
M. F. Mridha,Nilanjan DeyExplores cutting-edge medical imaging advancements and their applications in clinical decision-making.Addresses the development of multimodal machine learning models.Brings together a global network o作者: 袖章 時間: 2025-3-28 18:04 作者: endure 時間: 2025-3-28 21:39 作者: 清真寺 時間: 2025-3-29 01:24 作者: Agility 時間: 2025-3-29 06:51 作者: avenge 時間: 2025-3-29 10:40 作者: 接合 時間: 2025-3-29 13:21
Privacy-Preserving Vision-Based Detection of Pox Diseases Using Federated Learning,tential of federated learning to revolutionize disease diagnosis while preserving individual confidentiality. This research contributes to enhancing disease management and underscores the significance of privacy-aware healthcare technologies.作者: 發(fā)出眩目光芒 時間: 2025-3-29 16:37
,Unveiling the?Unique Dermatological Signatures of?Human Pox Diseases Through Deep Transfer Learningn human monkeypox, chickenpox, cowpox, measles, normal and hand-mouth face disease. Finally, our proposed model resulted in a test accuracy of 0.90, a precision of 0.89, a recall of 0.91, and an F1 score of 0.90, which significantly outperformed all other models, avoided common skin problems and exp作者: incarcerate 時間: 2025-3-29 20:53
,Improved Classification of?Kidney Lesions in?CT Scans Using CNN with?Attention Layers: Achieving Hi exceptional performance. With a remarkable accuracy percentage of 97.98%, and average precision, detection, and F1 score of 98%. The accuracy and performance metrics attained demonstrate the efficacy and promise of the suggested method in aiding healthcare practitioners in the preliminary evaluatio作者: 一加就噴出 時間: 2025-3-30 03:32
,Advancing Breast Cancer Diagnosis: Attention-Enhanced U-Net for?Breast Cancer Segmentation,significant potential for augmenting accuracy and resilience in analyzing medical images related to various organs. This is achieved by providing a mechanism to assimilate specialized knowledge tailored to specific tasks within deep learning frameworks. Additionally, our comparative analysis against作者: 阻撓 時間: 2025-3-30 05:46
,Privacy Preserving Breast Cancer Prediction with?Mammography Images Using Federated Learning,ographic data may lead to precision medicine. The goal is to improve patient quality of life, reduce mortality, and enhance early detection. With a dataset of four classes and 6,649 images, the model achieves 72.46% accuracy, laying the foundation for advanced privacy-preserving risk prediction mode作者: myelography 時間: 2025-3-30 08:43
,Improving Healthcare Efficiency via?Sensor-Based Remote Monitoring of?Patient Health Utilizing an?Eto healthcare services. This paper discusses the technical details of the proposed system and its potential impact on the healthcare industry. This paper also highlights future directions for research and development in this area.作者: 劇毒 時間: 2025-3-30 12:59
Data-Driven Clinical Decision-Making Using Deep Learning in Imaging作者: gonioscopy 時間: 2025-3-30 18:09 作者: Arrhythmia 時間: 2025-3-31 00:19
