Hybrid deep learning for detecting lung diseases from X-ray images
TLDR
In this article, the authors proposed a new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN, which is termed as VGG Data STN with CNN (VDSNet).About:
This article is published in Informatics in Medicine Unlocked.The article was published on 2020-01-01 and is currently open access. It has received 191 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
Performance of deep learning vs machine learning in plant leaf disease detection
TL;DR: This article is comparing the performance of ML (Support Vector Machine, Random Forest), Random Forest, Stochastic Gradient Descent (SGD), & DL (Inception-v3, V GG-16, VGG-19) in terms of citrus plant disease detection as DL methods perform better than that of ML methods in case of disease detection.
Journal ArticleDOI
A deep learning approach to detect Covid-19 coronavirus with X-Ray images.
TL;DR: An alternative diagnostic tool to detect COVID-19 cases utilizing available resources and advanced deep learning techniques is proposed in this work, and the efficacy of proposed method in present need of time is shown.
Journal ArticleDOI
A survey on deep learning in medicine: Why, how and when?
TL;DR: A comprehensive and in-depth study of Deep Learning methodologies and applications in medicine and how, where and why Deep Learning models are applied in medicine is presented.
Journal ArticleDOI
Medical image-based detection of COVID-19 using Deep Convolution Neural Networks.
TL;DR: This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN).
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Proceedings ArticleDOI
Mask R-CNN
TL;DR: This work presents a conceptually simple, flexible, and general framework for object instance segmentation, which extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.
Proceedings Article
Faster R-CNN: towards real-time object detection with region proposal networks
TL;DR: Ren et al. as discussed by the authors proposed a region proposal network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.