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:Â
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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Tuberculosis chest X-ray detection using CNN-based hybrid segmentation and classification approach
TL;DR: In this paper , the authors proposed TB-DenseNet which is based on five dual convolution blocks, DenseNet-169 layer, and a feature fusion block for the precise classification of tuberculosis images.
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B2-Net: An Artificial Intelligence Powered Machine Learning Framework for the Classification of Pneumonia in Chest X-ray Images
Abubeker K M,B. S +1 more
TL;DR: In this article , the B2-Net (Bek-Bas Network) model can differentiate between normal, bacterial, and viral pneumonia in chest X-ray images, achieving a remarkable 97.69% accuracy, 100% recall, and 0.9977 AUROC scores.
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Deep transfer learning CNN based approach for COVID-19 detection
TL;DR: To identify the COVID-19 symptoms with the help of a deep learning algorithm using chest X-Ray images, authors have further modified the pre-trained model with some extra CNN layers, such as the first layer is the average pooling layer and the other two are dense layers followed by ReLU with softmax activation function.
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Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models
TL;DR: A comparison of Deep Convolutional Neural Networks models for automatically binary classification query chest X-ray & CT images dataset with the goal of taking precision tools to health professionals based on fined recent versions of ResNet50, InceptionV3, and VGGNet is presented.
Proceedings ArticleDOI
Analysis of pneumonia detection systems using deep learning-based approach
A. Godbin,S. Graceline Jasmine +1 more
TL;DR: A detailed description of deep learning technologies used to treat pneumonia is presented and a discussion of how deep learning methods can be applicable to medical imaging, along with future challenges are discussed.
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