An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare.
TLDR
It is difficult to obtain a large amount of pneumonia dataset for this classification task, so several data augmentation algorithms were deployed to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.Abstract:
This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.read more
Citations
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Proceedings ArticleDOI
Optimal Pneumonia detection using Convolutional Neural Networks from X-ray Images
TL;DR: In this paper, the pretrained model of CNN is used for detection of pneumonia using chest Xray of normal and pneumonia affected persons, and compared among three optimizers, RMSProp Optimizer is providing more efficient model.
Journal ArticleDOI
Deep learning applied to automatic disease detection using chest X-rays.
TL;DR: Deep learning has shown rapid advancement and considerable promise when applied to the automatic detection of diseases using CXRs as mentioned in this paper, which is important given the widespread use of CXR across the world in diagnosing significant pathologies, and the lack of trained radiologists to report them.
Journal ArticleDOI
Fusion of convolutional neural networks based on Dempster–Shafer theory for automatic pneumonia detection from chest X-ray images
TL;DR: A new approach based on an evidence based fusion theory for the fusion of five pre trained convolutional neural networks allowing the combination of a set of deep learning classifiers to provide more accurate disease detection results.
Journal ArticleDOI
An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images
TL;DR: In this article, a model for screening TB using Chest X-ray images where the decisions from three base learners are combined using the type-1 Sugeno fuzzy integral based ensemble technique is proposed.
Journal ArticleDOI
An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images
TL;DR: In this article , the authors proposed a model for screening TB using Chest X-ray images where the decisions from three base learners are combined using the type-1 Sugeno fuzzy integral based ensemble technique.
References
More filters
Journal ArticleDOI
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
TL;DR: Quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures, including FCN and DeconvNet.
Journal ArticleDOI
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
Journal ArticleDOI
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan,Lily Peng,Marc Coram,Martin C. Stumpe,Derek Wu,Arunachalam Narayanaswamy,Subhashini Venugopalan,Kasumi Widner,Tom Madams,Jorge Cuadros,Ramasamy Kim,Rajiv Raman,Philip C. Nelson,Jessica L. Mega,Dale R. Webster +14 more
TL;DR: An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.
Proceedings Article
Neural Architecture Search with Reinforcement Learning
Barret Zoph,Quoc V. Le +1 more
TL;DR: In this paper, the authors use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set.
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