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Distributed Learning Approaches for Automated Chest X-Ray Diagnosis [article]

Edoardo Giacomello, Michele Cataldo, Daniele Loiacono, Pier Luca Lanzi
2021 arXiv   pre-print
performances of two recent distributed learning approaches - Federated Learning and Split Learning - on the task of Automated Chest X-Ray Diagnosis.  ...  Healthcare is one of the most promising field of application for Deep Learning approaches since it would allow to help clinicians to analyze patient data and perform diagnoses.  ...  AUTOMATED CHEST X-RAY INTERPRETATION Automated Chest X-Ray Diagnosis is the task of training a machine learning model capable of predicting a series of diseases given an input Chest X-Ray (CXR) image.  ... 
arXiv:2110.01474v1 fatcat:e4rdmpbqinejdk24s4ftav3spq

Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

Ahmed I. Iskanderani, Ibrahim M. Mehedi, Abdulah Jeza Aljohani, Mohammad Shorfuzzaman, Farzana Akther, Thangam Palaniswamy, Shaikh Abdul Latif, Abdul Latif, Aftab Alam, Dilbag Singh
2021 Journal of Healthcare Engineering  
The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak.  ...  The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset.  ...  Figure 1 (a) shows the manual chest X-ray modality analysis for the diagnosis of COVID-19 suspected subjects.  ... 
doi:10.1155/2021/3277988 pmid:34150188 pmcid:PMC8197673 fatcat:6lqbbsggknbfnk3aa4f3w2cfnq

Local Adaptation Improves Accuracy of Deep Learning Model for Automated X-Ray Thoracic Disease Detection : A Thai Study [article]

Isarun Chamveha, Trongtum Tongdee, Pairash Saiviroonporn, Warasinee Chaisangmongkon
2020 arXiv   pre-print
Here we present a wide-reaching development and testing of a deep learning algorithm for automated thoracic disease detection, utilizing 421,859 local chest radiographs.  ...  Our study shows that convolutional neural networks can achieve remarkable performance in detecting 13 common abnormality conditions on chest X-ray, and the incorporation of local images into the training  ...  Additional modern approaches to machine vision, such as attention-based mechanisms, super resolution, domain adaptation, explainable models, as well as label extraction techniques, are worth exploring  ... 
arXiv:2004.10975v3 fatcat:lz4ohaurdvfcdn3gmvoqlhepdi

Deep Learning for Automated Screening of Tuberculosis from Indian Chest X-rays: Analysis and Update [article]

Anushikha Singh, Brejesh Lall, B.K. Panigrahi, Anjali Agrawal, Anurag Agrawal, Balamugesh Thangakunam, DJ Christopher
2020 arXiv   pre-print
Methods: The proposed work explores the performance of convolutional neural networks (CNNs) for the diagnosis of TB in Indian chest x-ray images.  ...  To date, several deep learning based methods for automated detection of TB from chest radiographs have been proposed.  ...  DJ Christopher, and Balamugesh Thangakunam collected the Indian patients' chest X-ray images, interpreted, correlated with the clinical diagnosis & classified them. Dr.  ... 
arXiv:2011.09778v1 fatcat:6vjbn7blt5csnet552mzanmszm

Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks [article]

Narinder Singh Punn, Sonali Agarwal
2020 arXiv   pre-print
are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment.  ...  Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts  ...  We also would like to extend our thanks to the colleagues for their valuable guidance and suggestions.  ... 
arXiv:2004.11676v5 fatcat:7edpxvq4mrdy7obvf7m37tcrca

Automated identification of thoracic pathology from chest radiographs with enhanced training pipeline

Adora M. DSouza, Anas Z. Abidin, Axel Wismüller, Horst K. Hahn, Kensaku Mori
2019 Medical Imaging 2019: Computer-Aided Diagnosis  
Hence, a system for automated pre-reporting of pathologic findings on chest x-rays would greatly enhance radiologists' productivity.  ...  Chest x-rays are the most common radiology studies for diagnosing lung and heart disease.  ...  ACKNOWLEDGEMENTS This work is not being and has not been submitted for publication or presentation elsewhere. Pathology S1 [6]  ... 
doi:10.1117/12.2512600 dblp:conf/micad/DSouzaAW19 fatcat:55a5qijcobaw3m2phwr56p7pti

Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images

R Mohammadi
2020 Journal of Biomedical Physics and Engineering  
This study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection of COVID-19 infection in chest X-rays.  ...  In a retrospective study, we have applied Visual Geometry Group (VGG)-16, VGG-19, MobileNet, and InceptionResNetV2 pre-trained models for detection COVID-19 infection from 348 chest X-ray images.  ...  However, these approaches applied on chest X-ray images are very limited till now [15] .  ... 
doi:10.31661/jbpe.v0i0.2008-1153 pmid:33134214 pmcid:PMC7557468 fatcat:z2evpu3c4balvkofr4dfp2u3p4

Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks

Narinder Singh Punn, Sonali Agarwal
2020 Applied intelligence (Boston)  
are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment.  ...  Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts  ...  We also would like to extend our thanks to the colleagues for their valuable guidance and suggestions.  ... 
doi:10.1007/s10489-020-01900-3 pmid:34764554 pmcid:PMC7568031 fatcat:45rk4widujawlhxeprhhp6zgka

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  images [10134-129] 10134 3O Kernel descriptors for chest x-ray analysis [10134-130] 10134 3P A feasibility study for automatic lung nodule detection in chest digital tomosynthesis with machine  ...  assisted optical biopsy for colorectal polyps [10134-18] CARDIAC 10134 0K Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images [10134-19] 10134 0L Coronary  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4

CheXphoto: 10,000+ Photos and Transformations of Chest X-rays for Benchmarking Deep Learning Robustness [article]

Nick A. Phillips, Pranav Rajpurkar, Mark Sabini, Rayan Krishnan, Sharon Zhou, Anuj Pareek, Nguyet Minh Phu, Chris Wang, Mudit Jain, Nguyen Duong Du, Steven QH Truong, Andrew Y. Ng (+1 others)
2020 arXiv   pre-print
We release this dataset as a resource for testing and improving the robustness of deep learning algorithms for automated chest x-ray interpretation on smartphone photos of chest x-rays.  ...  Clinical deployment of deep learning algorithms for chest x-ray interpretation requires a solution that can integrate into the vast spectrum of clinical workflows across the world.  ...  Several recent advances in training deep learning algorithms for automated chest x-ray interpretation have been made possible by large datasets 3, 4 .  ... 
arXiv:2007.06199v2 fatcat:pryhvwbt6zbvhfeyq4qux4ehfu

CovFrameNet: An enhanced deep learning framework for COVID-19 detection

Olaide N. Oyelade, Absalom E. Ezugwu, Haruna Chiroma
2021 IEEE Access  
The National Institutes of Health (NIH) Chest X-Ray dataset and COVID-19 Radiography database were used to evaluate and validate the effectiveness of the proposed deep learning model.  ...  INDEX TERMS Image pre-processing, coronavirus, COVID-19, machine learning, deep learning, convolutional neural network, CNN, X-Ray.  ...  [27] applied CNN for the diagnosis of COVID-19 from X-Ray images. The approach combined learning and a pre-trained CNN encoder for extracting features representation.  ... 
doi:10.1109/access.2021.3083516 fatcat:tdivkbc5ufe4xhxz2nshpzgezm

Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

Hiam Alquran, Mohammad Alsleti, Roaa Alsharif, Isam Abu Qasmieh, Ali Mohammad Alqudah, Nor Hazlyna Binti Harun
2021 The MENDEL Soft Computing journal : International Conference on Soft Computing MENDEL  
To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized.  ...  The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify  ...  The automated system will detect disease based on the features in the enhanced image that will classify chest X-ray images into normal, pneumonia, or COVID-19.  ... 
doi:10.13164/mendel.2021.1.009 fatcat:svaunagobrgydbongyrjwwgdei

Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier

H. A. Owida, A. Al-Ghraibah, M. Altayeb
2021 Engineering, Technology & Applied Science Research  
However, COVID-19 is radiologically diagnosable using x-ray lung images. Deep learning methods have achieved cutting-edge performance in medical diagnosis software assistance.  ...  In this work, a new diagnostic method for detecting COVID-19 disease is implemented using advanced deep learning.  ...  These state of the art approaches are based on machine and deep learning approaches by using chest X-ray images.  ... 
doi:10.48084/etasr.4123 fatcat:6rlfdob6yzdbtlqyc7x5dh7equ

Deep Learning Model for Improving the Characterization of Coronavirus on Chest X-ray Images Using CNN [article]

Olaide Nathaniel Oyelade, Absalom E Ezugwu
2020 medRxiv   pre-print
The proposed model is then applied to the COVID-19 X-ray dataset in this study which is the National Institutes of Health (NIH) Chest X-Ray dataset obtained from Kaggle for the purpose of promoting early  ...  Different computational solutions comprised of natural language processing, knowledge engineering and deep learning have been adopted for this task.  ...  the Covid-19 Chest X-ray and NIH Chest X-ray datasets Figure 6 : Distribution of validation samples among classes of disease as drawn from the Covid-19 Chest X-ray and NIH Chest X-ray datasets .  ... 
doi:10.1101/2020.10.30.20222786 fatcat:3u7jrcseqrhjpcviqju6izqb3m

Automated detection of COVID-19 cases using deep neural networks with X-ray images

Tulin Ozturk, Muhammed Talo, Eylul Azra Yildirim, Ulas Baran Baloglu, Ozal Yildirim, U. Rajendra Acharya
2020 Computers in Biology and Medicine  
In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented.  ...  The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available.  ...  The model focuses on localizing effective regions on chest X-ray images. The proposed model can be used for the diagnosis of COVID-19 using X-ray radiographs.  ... 
doi:10.1016/j.compbiomed.2020.103792 pmid:32568675 pmcid:PMC7187882 fatcat:ydjzdgyxzfauhltehsrerepg5u
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