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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Proceedings ArticleDOI
An Intelligent Model to Predict Pneumonia Using Deep Extreme Machine Learning: *Note: Sub-titles are not captured in Xplore and should not be used
Faiza Khaliq,Muhammad Farhan Khan,Muhammad Sajid Farooq,Manas Ranjan Pradhan,Muhammad Asghar Khan,Tariq Rahim Somroo +5 more
TL;DR: In this paper , two deep learning models Alexnet and Xception are used and compared with the paper which has the same methodology and concludes the final accuracy and loss rate of pneumonia detection.
Book ChapterDOI
Clinical Decision Support Systems for Pneumonia Diagnosis Using Gradient-Weighted Class Activation Mapping and Convolutional Neural Networks
TL;DR: In this paper, a CNN architecture combining gradient-weighted class activation mapping (Grad-CAM) algorithm was proposed to discriminate between pneumonia patients and healthy controls in Chest X-ray images.
Journal ArticleDOI
Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey
Deepak Sinwar,V. S. Dhaka,Biniyam Tesfaye,Ghanshyam Raghuwanshi,Ashish Kumar,Sunil Kr. Maakar,Sanjay Agrawal +6 more
TL;DR: This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv, about the identification of COVID-19.
Book ChapterDOI
Pneumonia Identification from Chest X-rays (CXR) Using Ensemble Deep Learning Approach
TL;DR: Wang et al. as discussed by the authors designed a reliable image classifier for diagnosing pneumonia using an ensemble deep learning approach, and multiple experiments were conducted to evaluate transfer learning applications, data augmentations, and ensemble techniques.
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