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Deep Mask For X-ray Based Heart Disease Classification [article]

Xupeng Chen, Binbin Shi
2020 arXiv   pre-print
By learning the doctor's diagnostic experience, labeling the image and using tools to extract masks of heart region, we train a U-net to generate a mask to give more attention.  ...  We build a deep learning model to detect and classify heart disease using X-ray. We collect data from several hospitals and public datasets.  ...  However, there are not many studies on heart disease, especially the use of chest X-ray to diagnose heart disease.  ... 
arXiv:1808.08277v2 fatcat:7mmsnvmuzncxbf4p2uvntl4nia

Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization

Tawsifur Rahman, Amith Khandakar, Muhammad Abdul Kadir, Khandaker R. Islam, Khandaker F. Islam, Rashid Mazhar, Tahir Hamid, Mohammad T. Islam, Saad Kashem, Zaid B. Mahbub, Mohamed Arselene Ayari, Muhammad E. H. Chowdhury
2020 IEEE Access  
Firstly, two different U-net models were investigated for the segmentation of the chest X-ray images.  ...  It can be seen from Figure 7 that the original U-net model trained on Kaggle chest X-ray dataset can segment the lung areas of the X-ray images of the classification database very reliably.  ...  It was also shown that image segmentation can significantly improve classification accuracy.  ... 
doi:10.1109/access.2020.3031384 fatcat:fpn2e27glbgv7fp6gjujkrmjbm

Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network

Minki Kim, Byoung-Dai Lee
2021 Sensors  
Using X-ray images collected from public datasets for training and evaluation, we applied the proposed attention modules to U-Net for segmentation of lung areas and conducted experiments while changing  ...  In this study, we propose a deep learning-based method to segment lung areas in chest X-rays.  ...  segmentation results of the U-Net + X(1) + X(2) + Y(1) + Y(2) structure.ensors 2021, 21, x FOR PEER REVIEW 10 of Comparative performance of lung segmentation on chest X-ray images.  ... 
doi:10.3390/s21020369 pmid:33430480 fatcat:5ltb45wbabh4tfn5oc4kdua4ya

Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic

Sneha Kugunavar, C J Prabhakar
2021 Visual Computing for Industry, Biomedicine, and Art  
In this article, we present the application of CNNs for the diagnosis and prognosis of COVID-19 using X-ray and computed tomography (CT) images of COVID-19 patients.  ...  U-Net and voxel-based broad learning network were used for segmentation. Even with limited datasets, these methods proved to be beneficial for efficiently identifying the occurrence of COVID-19.  ...  While analyzing the modalities used for the experiment, chest X-rays are mainly used for detection, whereas CT scans are used for classification.  ... 
doi:10.1186/s42492-021-00078-w pmid:33950399 fatcat:msd5gal6vvdqzhasixgxd2pd5a

COVID-19 Automatic Diagnosis with Radiographic Imaging: Explainable AttentionTransfer Deep Neural Networks

Wenqi Shi, Li Tong, Yuanda Zhu, May Dongmei Wang
2021 IEEE journal of biomedical and health informatics  
Comprehensive experiments have been conducted on public chest X-ray and CT imaging datasets.  ...  This paper proposes an explainable attention transfer classification model based on the knowledge distillation network structure to automatically differentiate COVID-19, community acquired pneumonia (CAP  ...  Data Collection Our experiments are conducted on both COVID-19 related chest CT and X-ray image dataset separately.  ... 
doi:10.1109/jbhi.2021.3074893 pmid:33882010 pmcid:PMC8545079 fatcat:l6tx3cx4a5al5f64rqze4csy54

AIDCOV: An Interpretable Artificial Intelligence Model for Detection of COVID-19 from Chest Radiography Images [article]

Maryam Zokaeinikoo, Prasenjit Mitra, Soundar Kumara, Pooyan Kazemian
2020 medRxiv   pre-print
Therefore, automatic and accurate detection of COVID-19 using medical imaging modalities such as chest X-ray and Computed Tomography, which are more widely available and accessible, can be beneficial.  ...  We develop a novel hierarchical attention neural network model to classify chest radiography images as belonging to a person with either COVID-19, other infections, or no pneumonia (i.e., normal).  ...  Wang et al. developed COVID-Net using a deep convolutional neural network structure designed for detecting COVID-19 and other infections from chest X-ray images (Wang, Lin, and Wong 2020) .  ... 
doi:10.1101/2020.05.24.20111922 fatcat:x6pw5dzncbf2fpjmdnizv3q27m

A Survey on Machine Learning in COVID-19 Diagnosis

Xing Guo, Yu-Dong Zhang, Siyuan Lu, Zhihai Lu
2022 CMES - Computer Modeling in Engineering & Sciences  
Although the great achievements in medical images classification in recent years, Corona Virus Disease 2019 images classification based on machine learning still encountered many problems.  ...  First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification.  ...  chest X-ray images classification.  ... 
doi:10.32604/cmes.2022.017679 fatcat:hre5zxtekvaevleu335faqilwu

COVID-19 Imaging Detection in the Context of Artificial Intelligence and the Internet of Things

Xiaowei Gu, Shuwen Chen, Huisheng Zhu, Mackenzie Brown
2022 CMES - Computer Modeling in Engineering & Sciences  
Coronavirus disease 2019 brings a huge burden on the medical industry all over the world.  ...  In the background of artificial intelligence (AI) and Internet of Things (IoT) technologies, chest computed tomography (CT) and chest Xray (CXR) scans are becoming more intelligent, and playing an increasingly  ...  Using U-Net network and Seg-Net network for segmentation.  ... 
doi:10.32604/cmes.2022.018948 fatcat:tn3yyaoaujf2bla5qvg2f3532e

Towards Ignoring Backgrounds and Improving Generalization: a Costless DNN Visual Attention Mechanism [article]

Pedro R.A.S. Bassi, Andrea Cavalli
2022 arXiv   pre-print
We tested the ISNet with three applications: COVID-19 and tuberculosis detection in chest X-rays, and facial attribute estimation.  ...  This work introduces an attention mechanism for image classifiers and the corresponding deep neural network (DNN) architecture, dubbed ISNet.  ...  The previous study authors 18 trained the U-Net using 1263 chest X-rays representing four classes: COVID-19 (327 images), healthy (327 images), pneumonia (327 images), and tuberculosis (282 images).  ... 
arXiv:2202.00232v4 fatcat:tnsjygctz5hdhcscjpfknrawsm

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

Feng Shi, Jun Wang, Jun Shi, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, Dinggang Shen
2020 IEEE Reviews in Biomedical Engineering  
Also, AI can improve work efficiency by accurate delineation of infections in X-ray and CT images, facilitating subsequent quantification.  ...  Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further  ...  Chest X-ray images of 50 COVID-19 patients and 50 normal chest X-ray images are included.  ... 
doi:10.1109/rbme.2020.2987975 pmid:32305937 fatcat:cjswoasqh5b6hopdkcgceb5ca4

A cascade network for Detecting COVID-19 using chest x-rays [article]

Dailin Lv, Wuteng Qi, Yunxiang Li, Lingling Sun, Yaqi Wang
2020 arXiv   pre-print
We first used SEME-ResNet50 to screen chest X-ray and diagnosed three classes: normal, bacterial, and viral pneumonia.  ...  To exclude the influence of non-pathological features on the network, we preprocessed the data with U-Net during the training of SEME-DenseNet169.  ...  U-Net is used to remove non pathological features in X-ray films. Figure 2 : 2 Display of dataset1 and dataset2. Figure 3 : 3 Kmeans clustering graph of Imagenet, COVID-19 and other X-rays.  ... 
arXiv:2005.01468v1 fatcat:m3ykgrthrnbltln4hxb5zeytfu

Machine learning for medical imaging‐based COVID‐19 detection and diagnosis

Rokaya Rehouma, Michael Buchert, Yi‐Ping Phoebe Chen
2021 International Journal of Intelligent Systems  
Machine learning (ML) applications for COVID-19 diagnosis, detection, and the assessment of disease severity based on medical imaging have gained considerable attention.  ...  Herein, we review the recent progress of ML in COVID-19 detection with a particular focus on ML models using CT and X-ray images published in high-ranking journals, including a discussion of the predominant  ...  The third tree was able to distinguish X-rays that contained signs of COVID-19 with 95% accuracy. F I G U R E 4 Chest X-ray images.  ... 
doi:10.1002/int.22504 fatcat:chzex7hffbfmvfbmruxyf63lvq

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

Tudor Florin Ursuleanu, Andreea Roxana Luca, Liliana Gheorghe, Roxana Grigorovici, Stefan Iancu, Maria Hlusneac, Cristina Preda, Alexandru Grigorovici
2021 Diagnostics  
All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a  ...  The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the  ...  ) External medical datasets and natural images Recovery of medical image  brain tumor recovery [259]  X-ray image Recovery [18, 260]  image recovery with chest X-ray [261]  image recovery with  ... 
doi:10.3390/diagnostics11081373 fatcat:6p7usnvnxnewtivzeth745s3ga

Vision Transformers in Medical Computer Vision – A Contemplative Retrospection [article]

Arshi Parvaiz, Muhammad Anwaar Khalid, Rukhsana Zafar, Huma Ameer, Muhammad Ali, Muhammad Moazam Fraz
2022 arXiv   pre-print
These computer vision algorithms are being practised in medical image analysis and are transfiguring the perception and interpretation of Imaging data.  ...  We surveyed the application of Vision transformers in different areas of medical computer vision such as image-based disease classification, anatomical structure segmentation, registration, region-based  ...  For the classification of infected and non-infected chest x-rays, a methodology was introduced by Duong et al. [171] .  ... 
arXiv:2203.15269v1 fatcat:wecjpoikbvfz5cygytqpktoxdq

Learning to recognize Thoracic Disease in Chest X-rays with Knowledge-Guided Deep Zoom Neural Networks

Kun Wang, Xiaohong Zhang, Sheng Huang, Feiyu Chen, Xiangbo Zhang, Luwen Huangfu
2020 IEEE Access  
Automatic and accurate thorax disease diagnosis in Chest X-ray (CXR) image plays an essential role in clinical assist analysis.  ...  Also, we utilized weaklysupervised learning (WSL) to search for finer regions without using annotated samples. Learning on each scale consists of a classification sub-network.  ...  As can be seen, the U-Net can effectively localize in lung regions, ROC curves acquired by our proposed KGZNet on the Chest X-ray 14 dataset.  ... 
doi:10.1109/access.2020.3020579 fatcat:ep75ogtiwzfbvclenhm3wfe4te
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