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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Detection and classification of pneumonia in chest X-ray images by supervised learning
TL;DR: In this paper, a supervised computer aided diagnostic (CAD) system for the classification of the infected lung with pneumonia and the normal X-ray image was presented, which can process hundreds of X-rays images to extract features using the Histogram of Oriented Gradient (HOG) technique.
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Detection of pneumonia from x-ray images using deep learning techniques
TL;DR: In this article , an experimental study has been conducted for selecting the best artificial neural network ANN model that can be used for lung X-ray image classification, and the obtained best model has been used for classifying the lung x-ray images into three classes (Multi class classification) namely bacterial pneumonia, viral pneumonia, and healthy lung.
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A Robust Pneumonia Classification Approach based on Self-Paced Learning
TL;DR: This study proposes a self-paced learning scheme that integrates self-training and deep learning to select and learn labeled and unlabeled data samples for classifying anterior-posterior chest images as either being pneumonia-infected or normal.
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