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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Journal ArticleDOI
Modified Fuzzy Q Learning Based Classifier for Pneumonia and Tuberculosis
A. Kukker,R. Sharma +1 more
TL;DR: Modified fuzzy Q learning algorithm in conjunction with wavelet based pre-processing has been used to build a classfier for identifying pneumonia and tuberculosis's severity using a repository of X ray images.
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Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data
TL;DR: In this paper , a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) was proposed to detect the viral disease from chest X-rays (CXRs).
Journal ArticleDOI
Covid-19 Detection from Chest X-Ray using Convolution Neural Networks
TL;DR: A model is developed that automatically detect COVID and non-COVID X-rays and analytically determine the optimal CNN model for the purpose, which is used to validate the classification of the model.
Journal ArticleDOI
Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images
Chiagoziem C. Ukwuoma,Zhiguang Qin,Belal Bin Heyat,Faijan Akhtar,Abla Smahi,Jehoiada Jackson,Syed Furqan Qadri,A. Y. Muaad,Happy N. Monday,Grace U. Nneji +9 more
TL;DR: Wang et al. as discussed by the authors constructed a reliable deep-learning model capable of producing high classification accuracy on chest X-ray images for lung diseases, and the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling was applied to further derive higher global features of the images.
Proceedings ArticleDOI
Early pleural effusion detection from respiratory diseases including COVID-19 via deep learning
Sertan Serte,Ali Serener +1 more
TL;DR: In this article, the authors used deep learning to detect pleural effusion from tuberculosis, pneumonia, and COVID-19 diseases on chest radiographs, and the performance results showed that the deep learning architecture can distinguish bacterial pneumonia and co-vivo-19 disease from pleural fluid effusion the best.
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