Chest x-ray generation and data augmentation for cardiovascular abnormality classification

2018 ◽  
Author(s):  
Mehdi Moradi ◽  
Ali Madani ◽  
Alexandros Karargyris ◽  
Tanveer F. Syeda-Mahmood
Keyword(s):  
Data Augmentation ◽  
X Ray ◽  
Cardiovascular Abnormality ◽  
Chest X Ray

scholarly journals An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Okeke Stephen ◽  
Mangal Sain ◽  
Uchenna Joseph Maduh ◽  
Do-Un Jeong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Data Augmentation ◽  
Classification Performance ◽  
Learning Approaches ◽  
X Ray ◽  
Chest X Ray

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.

scholarly journals A Hybrid Capsule Network for Pneumonia Detection Using Image Augmentation Based on Generative Adversarial Network

2021 ◽  
Vol 38 (3) ◽  
pp. 619-627
Author(s):  
Kazim Firildak ◽  
Muhammed Fatih Talu
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Model Parameters ◽  
Classification Problems ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Discrimination Capability ◽  
Adversarial Network ◽  
Original Dataset ◽  
Chest X Ray

Pneumonia, featured by inflammation of the air sacs in one or both lungs, is usually detected by examining chest X-ray images. This paper probes into the classification models that can distinguish between normal and pneumonia images. As is known, trained networks like AlexNet and GoogleNet are deep network architectures, which are widely adopted to solve many classification problems. They have been adapted to the target datasets, and employed to classify new data generated through transfer learning. However, the classical architectures are not accurate enough for the diagnosis of pneumonia. Therefore, this paper designs a capsule network with high discrimination capability, and trains the network on Kaggle’s online pneumonia dataset, which contains chest X-ray images of many adults and children. The original dataset consists of 1,583 normal images, and 4,273 pneumonia images. Then, two data augmentation approaches were applied to the dataset, and their effects on classification accuracy were compared in details. The model parameters were optimized through five different experiments. The results show that the highest classification accuracy (93.91% even on small images) was achieved by the capsule network, coupled with data augmentation by generative adversarial network (GAN), using optimized parameters. This network outperformed the classical strategies.

scholarly journals Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection

2020 ◽  
Author(s):  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Data Augmentation ◽  
Feature Space ◽  
Distinct Pattern ◽  
Viral Pneumonia ◽  
Training Data ◽  
Polymerase Chain Reaction Test ◽  
X Ray ◽  
Chest X Ray ◽  
Baseline Test ◽  
Chain Reaction Test

AbstractThe novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused a pandemic resulting in over 2.7 million infected individuals and over 190,000 deaths and growing. Respiratory disorders in COVID-19 caused by the virus commonly present as viral pneumonia-like opacities in chest X-ray images which are used as an adjunct to the reverse transcription-polymerase chain reaction test for confirmation and evaluating disease progression. The surge places high demand on medical services including radiology expertise. However, there is a dearth of sufficient training data for developing image-based automated decision support tools to alleviate radiological burden. We address this insufficiency by expanding training data distribution through use of weakly-labeled images pooled from publicly available CXR collections showing pneumonia-related opacities. We use the images in a stage-wise, strategic approach and train convolutional neural network-based algorithms to detect COVID-19 infections in CXRs. It is observed that weakly-labeled data augmentation improves performance with the baseline test data compared to non-augmented training by expanding the learned feature space to encompass variability in the unseen test distribution to enhance inter-class discrimination, reduce intra-class similarity and generalization error. Augmentation with COVID-19 CXRs from individual collections significantly improves performance compared to baseline non-augmented training and weakly-labeled augmentation toward detecting COVID-19 like viral pneumonia in the publicly available COVID-19 CXR collections. This underscores the fact that COVID-19 CXRs have a distinct pattern and hence distribution, unlike non-COVID-19 viral pneumonia and other infectious agents.

scholarly journals Deep Learning Based Hybrid Models for Prediction of COVID-19 using Chest X-Ray

2020 ◽  
Author(s):  
Shree Charran R ◽  
Rahul Kumar Dubey
Keyword(s):  
Data Augmentation ◽  
False Negative ◽  
X Ray ◽  
Infected Case ◽  
X Ray Imaging ◽  
Normal Chest ◽  
Chest X Ray ◽  
To Come ◽  
Spread Of Infection

COVID-19 has ended up being the greatest pandemic to come to pass for on humanity in the last century. It has influenced all parts of present day life. The best way to confine its spread is the early and exact finding of infected patients. Clinical imaging strategies like Chest X-ray imaging helps specialists to assess the degree of spread of infection. In any case, the way that COVID-19 side effects imitate those of conventional Pneumonia brings few issues utilizing of Chest Xrays for its prediction accurately. In this investigation, we attempt to assemble 4 ways to deal with characterize between COVID-19 Pneumonia, NON-COVID-19 Pneumonia, and an Healthy- Normal Chest X-Ray images. Considering the low accessibility of genuine named Chest X-Ray images, we incorporated combinations of pre-trained models and data augmentation methods to improve the quality of predictions. Our best model has achieved an accuracy of 99.5216%. More importantly, the hybrid did not predict a False Negative Normal (i.e. infected case predicted as normal) making it the most attractive feature of the study.

scholarly journals Data Augmentation using Auxiliary Classifier for Improved Detection of Covid 19

2021 ◽  
Vol 10 (3) ◽  
pp. 209-214
Author(s):  
Lakshmisetty Ruthvik Raj ◽  
◽  
Bitra Harsha Vardhan ◽  
Mullapudi Raghu Vamsi ◽  
Keerthikeshwar Reddy Mamilla ◽  
...  
Keyword(s):  
Data Augmentation ◽  
X Rays ◽  
Convolutional Network ◽  
X Ray ◽  
Learning Network ◽  
X Ray Imaging ◽  
Abnormal Chest ◽  
Short Period ◽  
Chest X Ray ◽  
Deep Learning Network

COVID-19 is a severe and potentially fatal respiratory infection called coronavirus 2 disease (SARS-Co-2). COVID-19 is easily detectable on an abnormal chest x-ray. Numerous extensive studies have been conducted due to the findings, demonstrating how precise the detection of coronas using X-rays within the chest is. To train a deep learning network, such as a convolutional neural network, a large amount of data is required. Due to the recent end of the pandemic, it is difficult to collect many Covid x-ray images in a short period. The purpose of this study is to demonstrate how X-ray imaging (CXR) is created using the Covid CNN model-based convolutional network. Additionally, we demonstrate that the performance of CNNs and various COVID-19 acquisition algorithms can be used to generate synthetic images from data extensions. Alone, with CNN distribution, an accuracy of 85 percent was achieved. The accuracy has been increased to 95% by adding artificial images generated from data. We anticipate that this approach will expedite the discovery of COVID-19 and result in radiological solid programs. We leverage transfer learning in this paper to reduce time complexity and achieve the highest accuracy.

scholarly journals Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images

2020 ◽  
Author(s):  
Leonardo Rodrigues ◽  
Larissa Rodrigues ◽  
Danilo Da Silva ◽  
João Fernando Mari
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Relevant Information ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Disease Spread ◽  
X Ray ◽  
Chest X Ray ◽  
Scalable Methods

Coronavirus Disease 2019 (COVID-19) pandemic rapidly spread globally, impacting the lives of billions of people. The effective screening of infected patients is a critical step to struggle with COVID-19, and treating the patients avoiding this quickly disease spread. The need for automated and scalable methods has increased due to the unavailability of accurate automated toolkits. Recent researches using chest X-ray images suggest they include relevant information about the COVID-19 virus. Hence, applying machine learning techniques combined with radiological imaging promises to identify this disease accurately. It is straightforward to collect these images once it is spreadly shared and analyzed in the world. This paper presents a method for automatic COVID-19 detection using chest Xray images through four convolutional neural networks, namely: AlexNet, VGG-11, SqueezeNet, and DenseNet-121. This method had been providing accurate diagnostics for positive or negative COVID-19 classification. We validate our experiments using a ten-fold cross-validation procedure over the training and test sets. Our findings include the shallow fine-tuning and data augmentation strategies that can assist in dealing with the low number of positive COVID-19 images publicly available. The accuracy for all CNNs is higher than 97.00%, and the SqueezeNet model achieved the best result with 99.20%.

scholarly journals A Self-Attention Mechanism for COVID-19 Detection using Chest X-Ray Images

2021 ◽  
Author(s):  
Usman Muhammad ◽  
Md Ziaul Hoque ◽  
Mourad Oussalah ◽  
Anja Keskinarkaus ◽  
Tapio Seppänen ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Feature Space ◽  
Viral Disease ◽  
Attention Mechanism ◽  
Current Evidence ◽  
Great Promise ◽  
X Ray ◽  
Benchmark Datasets ◽  
Chest X Ray ◽  
Low Dimensional

<p>COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and infected cases have been escalated particularly in vulnerable states with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose an attention mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), an attention mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the attention mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on two publicly available databases to show that the proposed approach achieves the state-of-the-art results with an accuracy of 97% and 84% while being able to generate explanations. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots.<br></p>

scholarly journals Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8219
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Sultan Ahmad ◽  
Shakir Khan ◽  
Mohammed Ali Alshara ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
High Performance ◽  
Data Augmentation ◽  
Early Stage ◽  
Accurate Diagnosis ◽  
Fine Tuning ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.

scholarly journals A Multi-Stage GAN for Multi-Organ Chest X-ray Image Generation and Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2896
Author(s):  
Giorgio Ciano ◽  
Paolo Andreini ◽  
Tommaso Mazzierli ◽  
Monica Bianchini ◽  
Franco Scarselli
Keyword(s):  
Data Augmentation ◽  
Semantic Segmentation ◽  
Generative Adversarial Networks ◽  
Radiographic Images ◽  
X Ray ◽  
Adversarial Networks ◽  
Multi Stage ◽  
Segmentation Methods ◽  
Chest X Ray ◽  
Time Required

Multi-organ segmentation of X-ray images is of fundamental importance for computer aided diagnosis systems. However, the most advanced semantic segmentation methods rely on deep learning and require a huge amount of labeled images, which are rarely available due to both the high cost of human resources and the time required for labeling. In this paper, we present a novel multi-stage generation algorithm based on Generative Adversarial Networks (GANs) that can produce synthetic images along with their semantic labels and can be used for data augmentation. The main feature of the method is that, unlike other approaches, generation occurs in several stages, which simplifies the procedure and allows it to be used on very small datasets. The method was evaluated on the segmentation of chest radiographic images, showing promising results. The multi-stage approach achieves state-of-the-art and, when very few images are used to train the GANs, outperforms the corresponding single-stage approach.

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