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
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches.
TL;DR: With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.
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Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning
TL;DR: The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible.
Journal ArticleDOI
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks
TL;DR: A novel artificial neural network, Convolutional CapsNet for the detection of COVID-19 disease is proposed by using chest X-ray images with capsule networks to provide fast and accurate diagnostics for CO VID-19 diseases with binary classification, and multi-class classification.
Journal ArticleDOI
Attention-based VGG-16 model for COVID-19 chest X-ray image classification
TL;DR: A novel attention-based deep learning model using the attention module with VGG-16 that captures the spatial relationship between the ROIs in CXR images and indicates that it is suitable for CxR image classification in COVID-19 diagnosis.
Journal ArticleDOI
Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning.
Mohammad Farukh Hashmi,Satyarth Katiyar,Avinash G. Keskar,Neeraj Dhanraj Bokde,Zong Woo Geem +4 more
TL;DR: A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way and is able to outperform all the individual models.
References
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Proceedings ArticleDOI
Chest X-ray Image View Classification
Zhiyun Xue,Daekeun You,Sema Candemir,Stefan Jaeger,Sameer Antani,L. Rodney Long,George R. Thoma +6 more
TL;DR: A new method for classifying a CXR into two categories: frontal view vs. lateral view is presented and the features selected are image profile, body size ratio, pyramid of histograms of orientation gradients, and newly developed contour-based shape descriptor.
Proceedings ArticleDOI
Evaluation of Scan-Line Optimization for 3D Medical Image Registration
TL;DR: The presented algorithm, SGM-3D, employs a coarse-to-fine strategy and reduces the search space dimension for consecutive pyramid levels by a fixed linear rate, which allows it to handle large displacements to an extent that is required for clinical applications in high dimensional data.
Proceedings ArticleDOI
Fast and Exact: ADMM-Based Discriminative Shape Segmentation with Loopy Part Models
TL;DR: This work uses loopy part models to segment ensembles of organs in medical images and uses the Alternating Direction Method of Multipliers (ADMM) to fix the potential inconsistencies of the individual solutions and shows that ADMM yields substantially faster convergence than plain Dual Decomposition-based methods.
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