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
Performance Evaluation of the Deep Learning Based Convolutional Neural Network Approach for the Recognition of Chest X-Ray Images
Sandhya Rani Sharma,Sheifali Gupta,Deepali Gupta,Junaid Rashid,Jungeun Kim,Mahmoud Elarabawy +5 more
TL;DR: A CNN model is implemented for the recognition of Chest X-ray images for the detection of Pneumonia and the maximum recognition accuracy of 98% is obtained on the validation dataset.
Proceedings ArticleDOI
Pneumonia Detection Using Convolutional Neural Networks
TL;DR: Efficient, efficient, and cogent results are obtained by the proposed deep learning models to classify the chest X-rays for the detection of pneumonia.
Journal ArticleDOI
Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis
Surya Krishnamurthy,Kathiravan Srinivasan,Saeed Mian Qaisar,P. M. Durai Raj Vincent,Chuan-Yu Chang +4 more
TL;DR: In this paper, the authors compared various image classification models based on transfer learning with well-known deep learning architectures for pneumonitis classification from chest X-ray images and observed that the DenseNet201 model outperformed other models with an AUROC score of 0.966 and a recall score of0.99.
Book ChapterDOI
Convolutional Neural Network Based Chest X-Ray Image Classification for Pneumonia Diagnosis
TL;DR: The results indicate that the proposed Convolutional Neural Network based deep learning technique for the classification of chest X-ray images for the diagnosis of Pneumonia outperforms many of the popular models on several different performance parameters.
Journal ArticleDOI
Transfer Learning Based Model for Pneumonia Detection in Chest X-ray Images
TL;DR: A convolutional neural network-based model for reliably detecting pneumonic lungs from chest X-rays and achieves higher classification accuracy, precision, recall, and AUC values outperforming other state of art models with an overall accuracy of 97%.
References
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