Pre-trained convolutional neural networks as feature extractors for tuberculosis detection
TLDR
The focus of this work is to produce an investigation that will advance the research in the area, presenting three proposals to the application of pre-trained convolutional neural networks as feature extractors to detect the disease.About:
This article is published in Computers in Biology and Medicine.The article was published on 2017-10-01 and is currently open access. It has received 202 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Posted ContentDOI
Detection of Coronavirus Disease (COVID-19) Based on Deep Features
TL;DR: The deep learning based methodology is suggested for detection of coronavirus infected patient using X-ray images and the classification model ResNet50 plus SVM is superior compared to other classification models.
Journal ArticleDOI
Deep learning in medical imaging and radiation therapy.
Berkman Sahiner,Aria Pezeshk,Lubomir M. Hadjiiski,Xiaosong Wang,Karen Drukker,Kenny H. Cha,Ronald M. Summers,Maryellen L. Giger +7 more
TL;DR: The general principles of DL and convolutional neural networks are introduced, five major areas of application of DL in medical imaging and radiation therapy are surveyed, common themes are identified, methods for dataset expansion are discussed, and lessons learned, remaining challenges, and future directions are summarized.
Journal ArticleDOI
Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
TL;DR: An introduction to deep learning technology is provided and the stages that are entailed in the design process of deep learning radiology research are presented and the results of a survey of the application of convolutional neural networks to radiologic imaging are detailed.
Journal ArticleDOI
Detection of Coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine
TL;DR: The deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images and the method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people.
Journal ArticleDOI
Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization.
TL;DR: This work proposes a simple convolutional neural network optimized for the problem of tuberculosis diagnosis which is faster and more efficient than previous models but preserves their accuracy.
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.
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.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.