Journal ArticleDOI
Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives
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TLDR
An overview of the use of CNNs, for image classification, segmentation, detection, and other tasks such as registration, content-based image retrieval, image generation and enhancement, in some typical medical diagnosis areas such as brain, breast, and abdominal are presented.About:
This article is published in Neurocomputing.The article was published on 2021-07-15. It has received 103 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi,Jinglan Zhang,Amjad J. Humaidi,Ayad Q. Al-Dujaili,Ye Duan,Omran Al-Shamma,José Santamaría,Mohammed A. Fadhel,Muthana Al-Amidie,Laith Farhan +9 more
TL;DR: In this paper, a comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field is provided, and the challenges and suggested solutions to help researchers understand the existing research gaps.
Journal ArticleDOI
Federated Neural Architecture Search for Medical Data Security
TL;DR: Experimental verification demonstrates that the designed multiobjective CIT2FR-FL-NAS framework can achieve high accuracy superior to state-of-the-art models and reduce network complexity under the condition of protecting medical data security.
Journal ArticleDOI
Literature review: efficient deep neural networks techniques for medical image analysis
Journal ArticleDOI
Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.
Fatma Uysal Ciloglu,Abdullah Caliskan,Ayse Mine Saridag,İbrahim Kiliç,Mahmut Tokmakci,Mehmet Kahraman,Omer Aydin +6 more
TL;DR: Wang et al. as discussed by the authors proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surfaceenhanced Raman spectroscopy (SERS), which can learn features from raw data and classify them with an accuracy of 97.66%.
Journal ArticleDOI
Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
TL;DR: A detailed review of the past research papers using Machine Learning, Deep Learning and Deep Reinforcement Learning for BC classification and detection is carried out in this article , where critical analysis of the research and findings already done to detect and classify BC using various imaging modalities including "Mammography", "Histopathology", "Ultrasound", "PET/CT", "MRI, and Thermography".
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Freddie Bray,Jacques Ferlay,Isabelle Soerjomataram,Rebecca L. Siegel,Lindsey A. Torre,Ahmedin Jemal +5 more
TL;DR: A status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.