scholarly journals An integrative method for COVID-19 patients classification from chest X-ray using deep learning network with image visibility graph as feature extractor

2021 ◽  
Author(s):  
Mayukha Pal ◽  
Yash Tiwari ◽  
T Vineeth Reddy ◽  
Sai Ram Aditya Parisineni ◽  
Prasanta K Panigrahi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
False Negative ◽  
Haar Wavelet ◽  
Visibility Graph ◽  
Disease Classification ◽  
X Ray ◽  
Feature Extractor ◽  
Chest X Ray ◽  
Integrative Method

We propose a method by integrating image visibility graph and deep neural network (DL) for classifying COVID-19 patients from their chest X-ray images. The computed assortative coefficient from each image horizonal visibility graph (IHVG) is utilized as a physical parameter feature extractor to improve the accuracy of our image classifier based on Resnet34 convolutional neural network (CNN). We choose the most optimized recently used CNN deep learning model, Resnet34 for training the pre-processed chest X-ray images of COVID-19 and healthy individuals. Independently, the preprocessed X-ray images are passed through a 2D Haar wavelet filter that decomposes the image up to 3 labels and returns the approximation coefficients of the image which is used to obtain the horizontal visibility graph for each X-ray image of both healthy and COVID-19 cases. The corresponding assortative coefficients are computed for each IHVG and was subsequently used in random forest classifier whose output is integrated with Resnet34 output in a multi-layer perceptron to obtain the final improved prediction accuracy. We employed a multilayer perceptron to integrate the feature predictor from image visibility graph with Resnet34 to obtain the final image classification result for our proposed method. Our analysis employed much larger chest X-ray image dataset compared to previous used work. It is demonstrated that compared to Resnet34 alone our integrative method shows negligible false negative conditions along with improved accuracy in the classification of COVID-19 patients. Use of visibility graph in this model enhances its ability to extract various qualitative and quantitative complex network features for each image. Enables the possibility of building disease network model from COVID-19 images which is mostly unexplored. Our proposed method is found to be very effective and accurate in disease classification from images and is computationally faster as compared to the use of multimode CNN deep learning models, reported in recent research works.

scholarly journals MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

2021 ◽  
Author(s):  
Liangrui Pan ◽  
boya ji ◽  
Xiaoqi wang ◽  
shaoliang peng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Test Accuracy ◽  
Learning Networks ◽  
X Ray ◽  
Network Algorithm ◽  
Life Saving ◽  
Chest X Ray ◽  
Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

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 Klasifikasi Chest X-Ray Images Berdasarkan Kriteria Gejala Covid-19 Menggunakan Convolutional Neural Network

2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Vina Ayumi ◽  
Ida Nurhaida
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Output Parameter ◽  
X Ray ◽  
Chest X Ray ◽  
Fully Connected

Deteksi dini terhadap adanya indikasi pasien dengan gejala COVID-19 perlu dilakukan untuk mengurangi penyebaran virus. Salah satu cara yang dapat dilakukan untuk mendeteksi virus COVID-19 adalah dengan cara mempelajari citra chest x-ray pasien dengan gejala Covid-19. Citra chest x-ray dianggap mampu menggambarkan kondisi paru-paru pasien COVID-19 sebagai alat bantu untuk diagnosa klinis. Penelitian ini mengusulkan pendekatan deep learning berbasis convolutional neural network (CNN) untuk klasifikasi gejala COVID-19 melalui citra chest X-Ray. Evaluasi performa metode yang diusulkan akan menggunakan perhitungan accuracy, precision, recall, f1-score, dan cohens kappa. Penelitian ini menggunakan model CNN dengan 2 lapis layer convolusi dan maxpoling serta fully-connected layer untuk output. Parameter-parameter yang digunakan diantaranya batch_size = 32, epoch = 50, learning_rate = 0.001, dengan optimizer yaitu Adam. Nilai akurasi validasi (val_acc) terbaik diperoleh pada epoch ke-49 dengan nilai 0.9606, nilai loss validasi (val_loss) 0.1471, akurasi training (acc) 0.9405, dan loss training (loss) 0.2558.

scholarly journals An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 11 (21) ◽  
pp. 10301
Author(s):  
Muhammad Shoaib Farooq ◽  
Attique Ur Rehman ◽  
Muhammad Idrees ◽  
Muhammad Ahsan Raza ◽  
Jehad Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Early Stage ◽  
Binary Classification ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

scholarly journals Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Alexandros Karargyris ◽  
Satyananda Kashyap ◽  
Ismini Lourentzou ◽  
Joy T. Wu ◽  
Arjun Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Eye Tracking ◽  
Tracking System ◽  
Eye Gaze ◽  
Disease Classification ◽  
Radiology Report ◽  
Human Machine Interaction ◽  
Report Generation ◽  
X Ray ◽  
Chest X Ray

AbstractWe developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the following aligned data: CXR image, transcribed radiology report text, radiologist’s dictation audio and eye gaze coordinates data. We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning/machine learning methods. Furthermore, investigators in disease classification and localization, automated radiology report generation, and human-machine interaction can benefit from these data. We report deep learning experiments that utilize the attention maps produced by the eye gaze dataset to show the potential utility of this dataset.

scholarly journals DEEP LEARNING (CNN) MODEL FOR COVID-19 DETECTION FROM CHEST XRAY IMAGES

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Ahmed Mohamed ◽  
Ahmed Abdelhady
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Stage ◽  
Open Data ◽  
Disease Outbreak ◽  
Imaging Features ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Data Source

The Coronavirus disease outbreak result in many people to have severe respira- tory problems and it was recognized as a global health threat. Since the virus is targeting the lungs in the human body initially, chest x-ray imaging features were considered to be useful for the detection of the infection in the early stage. In this study, the chest x-ray data of 130 infected patients from an open data source that referenced Cohen J. Morrison P. Dao L., 2020 was used to build a CNN( Convolutional Neural-Network) model for the early detection of the disease. The model was trained with both infected and not-infected peoples’ chest x-ray images with 100 epochs which led to 0.98 accuracy finally. In order to use this model as a professional diagnosis element, it is highly recommended it be improved with more images and the model can be restructured to get a better accuracy.

scholarly journals Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset

2020 ◽  
Author(s):  
Lawrence Hall ◽  
Dmitry Goldgof ◽  
Rahul Paul ◽  
Gregory M. Goldgof
Keyword(s):  
Deep Learning ◽  
Bacterial Pneumonia ◽  
False Negative ◽  
False Negative Rate ◽  
X Rays ◽  
Test Results ◽  
X Ray ◽  
Chest X Ray ◽  
Small Dataset ◽  
Children Under 5

<p>Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 122 chest X-rays of COVID-19 and over 4,000 chest X-rays of viral and bacterial pneumonia. Unfortunately, we missed the fact that the chest X-rays of viral and bacterial pneumonia came from children under 5 years old. So, this work shows that you can tell kids with pneumonia from COVID-19 adult cases which is not anyone's goal. However, data from adult chest X-rays of other causes of lung disease is needed to see if you can tell adult diseases apart.<br></p>

scholarly journals A Novel Deep Learning Pipeline Architecture based on CNN to Detect Covid-19 in Chest X-ray Images

2021 ◽  
Vol 12 (6) ◽  
pp. 2001-2011
Author(s):  
Putra Sumari, Saqib Jamal Syed, Laith Abualigah
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Public Health Problem ◽  
Suitable Method ◽  
Main Diagnosis ◽  
Pipeline Architecture ◽  
X Ray ◽  
Chest X Ray ◽  
Detailed Diagnosis

Covid-19 is a severe public health problem worldwide. To date, it has spanned worldwide, with 24.6 million infected with 835,843 confirm the death. Covid-19 detection is indeed an important task and has to be done as quickly as possible so that treatment and monitoring can be carried out early. The current world standard RT-PCR screening for Covid-19 detection has to cope with the world population's great demand. There is a need to have an alternative way to cope with the demands. It has to be a quick and accurate detection procedure, such as using a chest x-ray for Covid-19 detection. This paper proposes a deep learning pipeline architecture called Gray Level Co-occurrence Matrix GLCM) with Convolutional Neural Network (CNN) for Covid-19 detection using chest X-ray image. The proposed method has two main diagnosis features, a quicker diagnosis, and a detailed diagnosis. The quicker diagnosis uses few GLCM features and a standard neural network (NN) algorithm to detect Covid-19 symptoms. It is a suitable method for rural areas where computing resources are minimal. The detailed diagnosis uses huge image pixel features and a deep convolutional neural network (CNN) algorithm to detect Covid-19 symptoms. It is a suitable method for places where computing resources are sufficient. The proposed work provides the highest classification performance, with 97.06% accuracy compared to other similar works.

scholarly journals COVID-19 Chest X-Ray Image Classification Using Deep Learning

2021 ◽  
Author(s):  
Soumava Dey ◽  
Gunther Correia Bacellar ◽  
Mallikarjuna Basappa Chandrappa ◽  
Raj Kulkarni
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Image Classification ◽  
Web Application ◽  
Human Error ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
X Ray ◽  
Chest X Ray ◽  
User Friendly

The rise of the coronavirus disease 2019 (COVID-19) pandemic has made it necessary to improve existing medical screening and clinical management of this disease. While COVID-19 patients are known to exhibit a variety of symptoms, the major symptoms include fever, cough, and fatigue. Since these symptoms also appear in pneumonia patients, this creates complications in COVID-19 detection especially during the flu season. Early studies identified abnormalities in chest X-ray images of COVID-19 infected patients that could be beneficial for disease diagnosis. Therefore, chest X-ray image-based disease classification has emerged as an alternative to aid medical diagnosis. However, manual detection of COVID-19 from a set of chest X-ray images comprising both COVID-19 and pneumonia cases is cumbersome and prone to human error. Thus, artificial intelligence techniques powered by deep learning algorithms, which learn from radiography images and predict presence of COVID-19 have potential to enhance current diagnosis process. Towards this purpose, here we implemented a set of deep learning pre-trained models such as ResNet, VGG, Inception and EfficientNet in conjunction with developing a computer vision AI system based on our own convolutional neural network (CNN) model: Deep Learning in Healthcare (DLH)-COVID. All these CNN models cater to image classification exercise. We used publicly available resources of 6,432 images and further strengthened our model by tuning hyperparameters to provide better generalization during the model validation phase. Our final DLH-COVID model yielded the highest accuracy of 96% in detection of COVID-19 from chest X-ray images when compared to images of both pneumonia-affected and healthy individuals. Given the practicality of acquiring chest X-ray images by patients, we also developed a web application (link: https://toad.li/xray) based on our model to directly enable users to upload chest X-ray images and detect the presence of COVID-19 within a few seconds. Taken together, here we introduce a state-of-the-art artificial intelligence-based system for efficient COVID-19 detection and a user-friendly application that has the capacity to become a rapid COVID-19 diagnosis method in the near future.

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