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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Book ChapterDOI
Intelligent System for Diagnosis of Pulmonary Tuberculosis Using XGBoosting Method
TL;DR: The extreme gradient boosting/XGBoosting model performed the highest testing score of AUC 95.86% using the full optimizing trained model as discussed by the authors , achieving high performance accuracy.
Journal ArticleDOI
A Portable Executable Clinical Decision Support Tool for Pneumonia Classification using Average Probability on an Ensemble Model
TL;DR: In this paper , a portable executable clinical decision support tool for chest X-ray image was developed to diagnose pneumonia in patients with low-prevalence presentations. But, there are certain factors that may increase the chances of a misdiagnosis such as fatigue, multi-presenting symptoms, or inadequate overall experience in assessment of patients having low-prediction presentations.
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Macrolide combination therapy for hospitalised CAP patients? An individualised approach supported by machine learning.
TL;DR: The article by König and co-workers paves the way for the selection of a subset of patients with CAP in whom combination initial therapy including macrolides could improve outcomes using a new and interesting mathematical approach.
Journal ArticleDOI
Learning effective embedding for automated COVID-19 prediction from chest X-ray images
TL;DR: Li et al. as mentioned in this paper adopted a transfer learning approach using pretrained models trained on imagenet dataset such as Alex Net and VGG16 as back-bone models and use data augmentation techniques to solve class imbalance and boost the classifier's performance.
Posted Content
Classification of Pneumonia and Tuberculosis from Chest X-rays.
TL;DR: In this article, the classification of pneumonia and tuberculosis from chest X-rays was performed using a machine learning model. But, the accuracy of the model was only 92.97%.
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