Journal ArticleDOI
An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks
Johnatan Carvalho Souza,João Otávio Bandeira Diniz,Jonnison Lima Ferreira,Giovanni Lucca França da Silva,Aristófanes Corrêa Silva,Anselmo Cardoso de Paiva +5 more
TLDR
The lung segmentation method demonstrates in this work that the problem of dense abnormalities in Chest X-rays can be efficiently addressed by performing a reconstruction step based on a deep convolutional neural network model.About:
This article is published in Computer Methods and Programs in Biomedicine.The article was published on 2019-08-01. It has received 167 citations till now. The article focuses on the topics: Segmentation.read more
Citations
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Journal ArticleDOI
Automated detection of COVID-19 cases using deep neural networks with X-ray images.
Tülin Öztürk,Muhammed Talo,Eylul Azra Yildirim,Ulas Baran Baloglu,Ozal Yildirim,U. Rajendra Acharya +5 more
TL;DR: A new model for automatic COVID-19 detection using raw chest X-ray images is presented and can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
Journal ArticleDOI
Deep Learning Approaches for COVID-19 Detection Based on Chest X-ray Images.
TL;DR: Results showed the deep approaches to be quite efficient when compared to the local texture descriptors in the detection of COVID-19 based on chest X-ray images.
Journal ArticleDOI
A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
Vikash Chouhan,Sanjay Kumar Singh,Aditya Khamparia,Deepak Gupta,Prayag Tiwari,Catarina Moreira,Robertas Damaševičius,Victor Hugo C. de Albuquerque +7 more
TL;DR: A novel deep learning framework for the detection of pneumonia using the concept of transfer learning, where features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction.
Journal ArticleDOI
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray
Tawsifur Rahman,Muhammad E. H. Chowdhury,Amith Khandakar,Khandakar Reajul Islam,Khandaker F. Islam,Zaid Bin Mahbub,Muhammad Abdul Kadir,Saad Bin Abul Kashem +7 more
TL;DR: The proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.
Journal ArticleDOI
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
Tawsifur Rahman,Muhammad E. H. Chowdhury,Amith Khandakar,Khandakar Reajul Islam,Khandaker F. Islam,Zaid Bin Mahbub,Muhammad Abdul Kadir,Saad Bin Abul Kashem +7 more
TL;DR: In this paper, the authors used four different pre-trained deep convolutional neural network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet for transfer learning.
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.
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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.
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings Article
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Xavier Glorot,Yoshua Bengio +1 more
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