標(biāo)題: Titlebook: Intelligent Vision in Healthcare; Mukesh Saraswat,Harish Sharma,Karm Veer Arya Book 2022 The Editor(s) (if applicable) and The Author(s), [打印本頁(yè)] 作者: 和尚吃肉片 時(shí)間: 2025-3-21 19:51
書目名稱Intelligent Vision in Healthcare影響因子(影響力)
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書目名稱Intelligent Vision in Healthcare網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Intelligent Vision in Healthcare被引頻次學(xué)科排名
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書目名稱Intelligent Vision in Healthcare年度引用學(xué)科排名
書目名稱Intelligent Vision in Healthcare讀者反饋
書目名稱Intelligent Vision in Healthcare讀者反饋學(xué)科排名
作者: NOTCH 時(shí)間: 2025-3-21 22:35 作者: landmark 時(shí)間: 2025-3-22 03:19
,Predicting Heart Disease with?Multiple Classifiers,rest, K-nearest neighbor, and logistic regression. The final class of a new instance is that predicted by the maximum weighted sum of predictions from the classifiers. This technique is compared with already existing methods, and an improvement in accuracy (92.10%) and sensitivity (94.59%) and a dra作者: Deject 時(shí)間: 2025-3-22 07:47 作者: insightful 時(shí)間: 2025-3-22 12:19 作者: 鐵砧 時(shí)間: 2025-3-22 13:47
Automatic Brain Tumor Classification in 2D MRI Images Using Integrated Deep Learning and Supervisedop, and detection accuracy of 98.1%, 92.5%, and 83.0% is achieved. The features are extracted using CNN, and tumor detection is done by using four supervised machine learning classifiers. The classifiers used are SVM, KNN classifier, Na?ve Bayes classifier, and discriminant analysis. The accuracy ac作者: inspiration 時(shí)間: 2025-3-22 18:00 作者: Commonplace 時(shí)間: 2025-3-23 00:04
P. Hosanna Princye,M. Lavanya,S. Siva Subramanian,M. Arivalagan,S. Bagyarajg that blurs the line between truth and falsehood with increasingly powerful strategies supported by artificial intelligence..978-3-030-81570-7978-3-030-81568-4Series ISSN 2945-6118 Series E-ISSN 2945-6126 作者: Nonthreatening 時(shí)間: 2025-3-23 05:07
M. Ganeshkumar,V. Sowmya,E. A. Gopalakrishnan,K. P. Soman作者: crescendo 時(shí)間: 2025-3-23 06:56 作者: 修剪過的樹籬 時(shí)間: 2025-3-23 11:51
K. Benaggoune,Z. Al Masry,C. Devalland,S. Valmary-degano,N. Zerhouni,L. H. Mouss作者: CORD 時(shí)間: 2025-3-23 16:12 作者: 邊緣 時(shí)間: 2025-3-23 18:10 作者: Mercurial 時(shí)間: 2025-3-24 02:14 作者: curriculum 時(shí)間: 2025-3-24 03:01 作者: 緩和 時(shí)間: 2025-3-24 10:05 作者: collagenase 時(shí)間: 2025-3-24 10:44
Pramit Ghosh,Debotosh Bhattacharjee,Christian Kollmannue based on their own ideologies, all of which leads them to seek media presence..This chapter will address lobbies in two ways: in political media channels and organizational political communication, especially during the pre-campaign and campaign season. In it, we provide an overview of the trends作者: 路標(biāo) 時(shí)間: 2025-3-24 17:17 作者: micronized 時(shí)間: 2025-3-24 21:57
2730-6437 nd day-to-day life. It also highlights many challenges faced by research community, like view point variations, scale variations, illumination variations, multi-modalities, and noise..978-981-16-7773-1978-981-16-7771-7Series ISSN 2730-6437 Series E-ISSN 2730-6445 作者: 淺灘 時(shí)間: 2025-3-24 23:45 作者: 戲法 時(shí)間: 2025-3-25 06:57 作者: TAP 時(shí)間: 2025-3-25 10:14 作者: Detonate 時(shí)間: 2025-3-25 12:20 作者: 慢慢沖刷 時(shí)間: 2025-3-25 16:08 作者: Irritate 時(shí)間: 2025-3-25 22:21
,Deep Learning-Based Prediction of?Alzheimer’s Disease from?Magnetic Resonance Images,rning show good results. These network architectures are taken and re-trained using brain images. It is shown that a deep ResNet neural architecture performs better in terms of accuracy. Kaggle dataset was used as the dataset to conduct our experiments.作者: Mitigate 時(shí)間: 2025-3-26 01:40 作者: 躺下殘殺 時(shí)間: 2025-3-26 05:40 作者: Feedback 時(shí)間: 2025-3-26 09:17
2730-6437 f medical science.Highlights many challenges faced by researThis book focuses on various aspects of computer vision applications in the field of healthcare. It covers new tools and technologies in some of the important areas of medical science like histopathological image analysis, cancer taxonomy, 作者: 虛構(gòu)的東西 時(shí)間: 2025-3-26 15:19 作者: investigate 時(shí)間: 2025-3-26 20:09
Studies in Autonomic, Data-driven and Industrial Computinghttp://image.papertrans.cn/i/image/470193.jpg作者: Adulate 時(shí)間: 2025-3-26 21:57
https://doi.org/10.1007/978-981-16-7771-7Deep Learning; Convolutional Neural Network; Computer Vision; High Level Image Processing; Machine Learn作者: 固執(zhí)點(diǎn)好 時(shí)間: 2025-3-27 02:47
978-981-16-7773-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: SHOCK 時(shí)間: 2025-3-27 06:28 作者: fringe 時(shí)間: 2025-3-27 10:01 作者: grenade 時(shí)間: 2025-3-27 15:36 作者: arousal 時(shí)間: 2025-3-27 21:45
,Unsupervised Deep Learning Approach for?the?Identification of?Intracranial Haemorrhage in?CT Imagesset is a common issue for diseases like ICH. To overcome this issue, in this work, we propose a completely unsupervised deep learning framework for the identification of ICH in computed tomography (CT) images. Our proposed method employs unsupervised (principal component analysis) PCA-Net to extract作者: debris 時(shí)間: 2025-3-27 22:26
Automatic Segmentation of Optic Cup and Optic Disc Using MultiResUNet for Glaucoma Classification fanently. In India, the ophthalmologists are limited in numbers to check the patient. Owing to this, automated detection of glaucoma from the fundus images of the eye region is the state of the art in medical imaging. In this work, the most common method for detection of glaucoma is a parameter calle作者: 做事過頭 時(shí)間: 2025-3-28 04:49 作者: gusher 時(shí)間: 2025-3-28 08:41
,Predicting Heart Disease with?Multiple Classifiers, hundred million in the previous decade. Given that data is generated daily by several sources and in various formats, its timely, accurate, and cost-efficient prediction is imperative for clinical reforms. Machine learning is reputable for its effectiveness in the prediction of medical conditions b作者: 確保 時(shí)間: 2025-3-28 12:18 作者: ELUC 時(shí)間: 2025-3-28 16:40
Automatic True Vessel Identification by Efficient Removal of False Blood Vessels for Detection of Rtially, the hybrid median filtering is employed for the noise removal and hybrid independent component analysis is used for image enhancement in the stage of preprocessing. Further from the preprocessed images, the OD segmentation is performed using DAF and modified bee colony algorithm. Then, the f作者: 陰郁 時(shí)間: 2025-3-28 20:40 作者: 燈絲 時(shí)間: 2025-3-29 00:24 作者: tariff 時(shí)間: 2025-3-29 06:20 作者: 制定 時(shí)間: 2025-3-29 10:22 作者: 某人 時(shí)間: 2025-3-29 12:02
tional feminism and cyberfeminism.Addresses issues of entrepThis project offers a critical overview of how online activities and platforms are becoming an important source for the production and promotion of women’s films. Inspired by a transnational feminist framework, Maule examines blogs, website作者: kidney 時(shí)間: 2025-3-29 17:54 作者: Explicate 時(shí)間: 2025-3-29 20:18 作者: 描繪 時(shí)間: 2025-3-30 00:26
Pramit Ghosh,Debotosh Bhattacharjee,Christian Kollmann and, until 2008, the nearly complete lack of EU legislation regulating their transparency, very few scientific studies have addressed these influential organizations. Though not well-known, their significance and relevance have become increasingly important in the development, introduction, and imp
