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
A Practical Deep Learning Model in Differentiating Pneumonia-Type Lung Carcinoma from Pneumonia on CT Images: ResNet Added with Attention Mechanism
Wang Du,Xiao-hua Luo,Min Chen +2 more
TL;DR: This deep learning model could detect pneumonia-type lung carcinoma and differentiate it from pneumonia accurately and efficiently.
Journal ArticleDOI
Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models
TL;DR: In this article , the Synthetic Minority Oversampling Technique (SMOTE) approach is used to eliminate the class imbalance in the X-ray dataset, and an ensemble CNN model is developed.
Journal ArticleDOI
Chest Radiographs Using a Context-Fusion Convolution Neural Network (CNN): Can It Distinguish the Etiology of Community-Acquired Pneumonia (CAP) in Children?
TL;DR: In this paper , a context-fusion convolution neural network (CNN) was used to distinguish the etiology of community-acquired pneumonia in children using chest radiographs.
Journal ArticleDOI
Survival study on deep learning techniques for IoT enabled smart healthcare system
Ashok Kumar Munnangi,Satheeshwaran UdhayaKumar,Vinayakumar Ravi,Ramesh Sekaran,Suthendran Kannan +4 more
Book ChapterDOI
Pneumonia Detection from Chest X-Ray Images Using Deep Learning Methods
TL;DR: In this article , an efficient architecture that is trained on chest X-rays in digital forms is applied for detecting pneumonia which can aid the radiology staff in the process of making the final decision.
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