1,676 research outputs found

    PadChest: A large chest x-ray image dataset with multi-label annotated reports

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    We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/

    Deep learning classification of chest x-ray images

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    We propose a deep learning based method for classification of commonly occurring pathologies in chest X-ray images. The vast number of publicly available chest X-ray images provides the data necessary for successfully employing deep learning methodologies to reduce the misdiagnosis of thoracic diseases. We applied our method to the classification of two example pathologies, pulmonary nodules and cardiomegaly, and we compared the performance of our method to three existing methods. The results show an improvement in AUC for detection of nodules and cardiomegaly compared to the existing methods.Comment: 4 pages, 4 figures, 2 tables, conference , SSIAI 202

    Image Features for Tuberculosis Classification in Digital Chest Radiographs

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    Tuberculosis (TB) is a respiratory disease which affects millions of people each year, accounting for the tenth leading cause of death worldwide, and is especially prevalent in underdeveloped regions where access to adequate medical care may be limited. Analysis of digital chest radiographs (CXRs) is a common and inexpensive method for the diagnosis of TB; however, a trained radiologist is required to interpret the results, and is subject to human error. Computer-Aided Detection (CAD) systems are a promising machine-learning based solution to automate the diagnosis of TB from CXR images. As the dimensionality of a high-resolution CXR image is very large, image features are used to describe the CXR image in a lower dimension while preserving the elements in the CXR necessary for the detection of TB. In this thesis, I present a set of image features using Pyramid Histogram of Oriented Gradients, Local Binary Patterns, and Principal Component Analysis which provides high classifier performance on two publicly available CXR datasets, and compare my results to current state-of-the-art research

    Feature Detection in Medical Images Using Deep Learning

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    This project explores the use of deep learning to predict age based on pediatric hand X-Rays. Data from the Radiological Society of North America’s pediatric bone age challenge were used to train and evaluate a convolutional neural network. The project used InceptionV3, a CNN developed by Google, that was pre-trained on ImageNet, a popular online image dataset. Our fine-tuned version of InceptionV3 yielded an average error of less than 10 months between predicted and actual age. This project shows the effectiveness of deep learning in analyzing medical images and the potential for even greater improvements in the future. In addition to the technological and potential clinical benefits of these methods, this project will serve as a useful pedagogical tool for introducing the challenges and applications of deep learning to the Bryant community

    Tuberculosis Disease Detection through CXR Images based on Deep Neural Network Approach

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    Tuberculosis (TB) is a disease that, if left untreated for an extended period of time, can ultimately be fatal. Early TB detection can be aided by using a deep learning ensemble. In previous work, ensemble classifiers were only trained on images that shared similar characteristics. It is necessary for an ensemble to produce a diverse set of errors in order for it to be useful; this can be accomplished by making use of a number of different classifiers and/or features. In light of this, a brand-new framework has been constructed in this study for the purpose of segmenting and identifying TB in human Chest X-ray. It was determined that searching traditional web databases for chest X-ray was necessary. At this point, we pass the photos that we have collected over to Swin ResUnet3 so that they may be segmented. After the segmented chest X-ray have been provided to it, the Multi-scale Attention-based Densenet with Extreme Learning Machine (MAD-ELM) model will be applied in the detection stage in order to effectively diagnose tuberculosis from human chest X-ray. This will be done in order to maximize efficiency. Because it increased the variety of errors made by the basic classifiers, the supplied variation of the approach that was proposed was able to detect tuberculosis more effectively. The proposed ensemble method produced results with an accuracy of 94.2 percent, which are comparable to those obtained by past efforts

    Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

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    Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy
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