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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Pneumonia Detection Proposing a Hybrid Deep Convolutional Neural Network Based on Two Parallel Visual Geometry Group Architectures and Machine Learning Classifiers
TL;DR: A novel hybrid Convolutional Neural Network (CNN) model is proposed using three classification approaches and suggests that the proposed ensemble classifier using Support Vector Machine with Radial Basis Function and Logistic Regression classifiers has the best performance with 98.55% accuracy.
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COVID-19 detection using chest X-ray images based on a developed deep neural network
TL;DR: In this paper , a Convolutional Neural Network-Long Short Time Memory (LSTM) model is proposed to extract features from raw chest X-ray images hierarchically.
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Using artificial intelligence technology to fight COVID-19: a review
TL;DR: In this article , the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions.
Proceedings ArticleDOI
Prediction of Pneumonia Using Big Data, Deep Learning and Machine Learning Techniques
TL;DR: In this article, the authors presented the prediction of pneumonia using big data, deep learning and machine learning techniques, which is one of the most active areas of research in order to improve the health and the medical science.
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A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images
Chiagoziem C. Ukwuoma,Zhiguang Qin,Belal Bin Heyat,Faijan Akhtar,Olusola Bamisile,A. Y. Muaad,Daniel Addo,Mugahed A. Al-antari +7 more
TL;DR: Chima et al. as discussed by the authors proposed a new hybrid explainable deep learning framework for accurate pneumonia disease identification using chest X-ray images by fusing the capabilities of both ensemble convolutional networks and the Transformer Encoder mechanism.
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