scholarly journals Pneumonia Detection using Chest X-Ray Images

2021 ◽  
Vol 9 (VI) ◽  
pp. 1611-1618
Author(s):  
Shruti Meshram
Keyword(s):  
Medical Center ◽  
Network Models ◽  
Ensemble Model ◽  
X Rays ◽  
Neural Network Models ◽  
X Ray ◽  
Learning Framework ◽  
Unseen Data ◽  
Chest X Ray ◽  
Bacteria And Fungi

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pre-trained on Image Net, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pre-trained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.

scholarly journals A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

2020 ◽  
Vol 10 (2) ◽  
pp. 559 ◽  
Author(s):  
Vikash Chouhan ◽  
Sanjay Kumar Singh ◽  
Aditya Khamparia ◽  
Deepak Gupta ◽  
Prayag Tiwari ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Medical Center ◽  
Network Models ◽  
Ensemble Model ◽  
X Rays ◽  
Neural Network Models ◽  
Learning Framework ◽  
Unseen Data ◽  
Chest X Ray ◽  
Bacteria And Fungi

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.

scholarly journals Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study

2020 ◽  
Vol 20 (S14) ◽  
Author(s):  
Qingfeng Wang ◽  
Qiyu Liu ◽  
Guoting Luo ◽  
Zhiqin Liu ◽  
Jun Huang ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Automatic Segmentation ◽  
Medical Emergency ◽  
Dice Similarity Coefficient ◽  
X Rays ◽  
X Ray ◽  
Learning Framework ◽  
Multi Scale ◽  
Life Threatening ◽  
Chest X Ray

Abstract Background Pneumothorax (PTX) may cause a life-threatening medical emergency with cardio-respiratory collapse that requires immediate intervention and rapid treatment. The screening and diagnosis of pneumothorax usually rely on chest radiographs. However, the pneumothoraces in chest X-rays may be very subtle with highly variable in shape and overlapped with the ribs or clavicles, which are often difficult to identify. Our objective was to create a large chest X-ray dataset for pneumothorax with pixel-level annotation and to train an automatic segmentation and diagnosis framework to assist radiologists to identify pneumothorax accurately and timely. Methods In this study, an end-to-end deep learning framework is proposed for the segmentation and diagnosis of pneumothorax on chest X-rays, which incorporates a fully convolutional DenseNet (FC-DenseNet) with multi-scale module and spatial and channel squeezes and excitation (scSE) modules. To further improve the precision of boundary segmentation, we propose a spatial weighted cross-entropy loss function to penalize the target, background and contour pixels with different weights. Results This retrospective study are conducted on a total of eligible 11,051 front-view chest X-ray images (5566 cases of PTX and 5485 cases of Non-PTX). The experimental results show that the proposed algorithm outperforms the five state-of-the-art segmentation algorithms in terms of mean pixel-wise accuracy (MPA) with $$0.93\pm 0.13$$ 0.93 ± 0.13 and dice similarity coefficient (DSC) with $$0.92\pm 0.14$$ 0.92 ± 0.14 , and achieves competitive performance on diagnostic accuracy with 93.45% and $$F_1$$ F 1 -score with 92.97%. Conclusion This framework provides substantial improvements for the automatic segmentation and diagnosis of pneumothorax and is expected to become a clinical application tool to help radiologists to identify pneumothorax on chest X-rays.

scholarly journals Fast COVID-19 Detection of Chest X-Ray Images Using Single Shot Detection MobileNet Convolutional Neural Networks

2021 ◽  
Vol 56 (2) ◽  
pp. 235-248
Author(s):  
Fatchul Arifin ◽  
Herjuna Artanto ◽  
Nurhasanah ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Network Models ◽  
Single Shot ◽  
Neural Network Models ◽  
X Ray ◽  
Detection Model ◽  
Shot Detection ◽  
Average Accuracy ◽  
Chest X Ray

COVID-19 is a new disease with a very rapid and tremendous spread. The most important thing needed now is a COVID-19 early detection system that is fast, easy to use, portable, and affordable. Various studies on desktop-based detection using Convolutional Neural Networks have been successfully conducted. However, no research has yet applied mobile-based detection, which requires low computational cost. Therefore, this research aims to produce a COVID-19 early detection system based on chest X-ray images using Convolutional Neural Network models to be deployed in mobile applications. It is expected that the proposed Convolutional Neural Network models can detect COVID-19 quickly, economically, and accurately. The used architecture is MobileNet's Single Shot Detection. The advantage of the Single Shot Detection MobileNet models is that they are lightweight to be applied to mobile-based devices. Therefore, these two versions will also be tested, which one is better. Both models have successfully detected COVID-19, normal, and viral pneumonia conditions with an average overall accuracy of 93.24% based on the test results. The Single Shot Detection MobileNet V1 model can detect COVID-19 with an average accuracy of 83.7%, while the Single Shot Detection MobileNet V2 Single Shot Detection model can detect COVID-19 with an average accuracy of 87.5%. Based on the research conducted, it can be concluded that the approach to detecting chest X-rays of COVID-19 can be detected using the MobileNet Single Shot Detection model. Besides, the V2 model shows better performance than the V1. Therefore, this model can be applied to increase the speed and affordability of COVID-19 detection.

scholarly journals APPLICATION OF NEURAL NETWORKS TO DETERMINE THE COORDINATES OF THE SEAT OF FIRE BY MULTIPOINT ELECTRO-OPTICAL SYSTEM

2021 ◽  
pp. 248-251
Author(s):  
Д.Ф. Пирова ◽  
Б.Э. Забержинский ◽  
А.Г. Золин
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
Network Models ◽  
X Rays ◽  
Neural Network Models ◽  
X Ray ◽  
Python Programming Language ◽  
Python Programming

Статья посвящена исследованию методов проектирования интеллектуальных информационных систем и применение моделей искусственных нейронных сетей для диагностического прогнозирования развития пневмонии посредством анализа рентгеновских снимков. В этой работе основное внимание уделяется классификации пневмонии и туберкулеза - двух основных заболеваний грудной клетки - на основе рентгеновских снимков грудной клетки. Данное исследование проводилось при помощи открытой нейросетевой библиотеки Keras и языка программирования Python. Система дает пользователю заключение о том, болен он или нет, тем самым помогая врачам и медицинскому персоналу принять быстрое и информированное решение о наличии заболевания. Разработанная модель, может определить, является ли рентгеновский снимок нормальным или имеет отклонения, которые могут быть пневмонией с точностью 94,87%. Полученные результаты указывают на высокую эффективность применения нейронных сетей при диагностировании пневмонии по рентгеновским снимкам. This paper is devoted to the study of methods of designing intellectual information systems and neural network models application on diagnostic prediction of pneumonia development by X-ray images analysis. This article focuses on the classification of pneumonia and tuberculosis - the two main chest diseases - based on chest x-rays. This study was carried out using the Keras open neural network library and the Python programming language. System returns user a conclusion whether the patient is ill or not helping medical staff to make a quick and informed decision about the presence of the disease. The developed model can determine is the X-ray image normal or has anomalies that can be pneumonia with accuracy up to 94.87%. The results obtained indicate the high performance of the applying neural networks in the diagnosis of pneumonia by X-ray images.

scholarly journals A Novel Normalization Algorithm to Facilitate Pre-Assessment of Covid-19 Disease from Chest X-ray Image and its FPGA implementation

2022 ◽  
Author(s):  
Sertaç Yaman ◽  
Barış Karakaya ◽  
yavuz erol
Keyword(s):  
Network Models ◽  
Full Time ◽  
Number Representation ◽  
Neural Network Models ◽  
Human Errors ◽  
X Ray ◽  
Python Language ◽  
The Neural Network ◽  
Human Lungs ◽  
Chest X Ray

Abstract COVID-19 is still a fatal disease, which has threatened all people by affecting the human lungs. Chest X-Ray or computed tomography (CT) imaging is commonly used to make a fast and reliable medical investigation to detect the COVID-19 virus from these medical images is remarkably challenging because it is a full-time job and prone to human errors. In this paper, a new normalization algorithm that consists of Mean-Variance-Softmax-Rescale (MVSR) processes respectively is proposed to provide facilitation pre-assessment and diagnosis Covid-19 disease. In order to show the effect of MVSR normalization technique on image processing, the algorithm is applied to chest X-ray images. Therefore, the normalized X-ray images with MVSR are used to recognize via one of the neural network models as known Convolutional Neural Networks (CNNs). At the implementation stage, the MVSR algorithm is executed on MATLAB environment, then it is implemented on FPGA platform. All the arithmetic operations of the MVSR normalization are coded in VHDL with the help of fixed-point fractional number representation format. The experimental platform consists of Zynq-7000 Development Board and VGA monitor to display the both original X-ray and MVSR normalized image. The CNN model is constructed and executed using Anaconda Navigator interface with python language. Based on the results of this study, infections of Covid-19 disease can be easily diagnosed for MVSR normalized image. The proposed MVSR normalization makes the accuracy of CNN model increase from 83.01%, to 96.16% for binary class of chest X-ray images.

scholarly journals Pneumonia Detection from Chest X-Rays using Neural Networks

2021 ◽  
Vol 9 (VII) ◽  
pp. 910-913
Author(s):  
Ashish Jain
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Great Success ◽  
X Rays ◽  
X Ray ◽  
Open Set ◽  
The World ◽  
Chest X Ray ◽  
Bacteria And Fungi ◽  
Inflammation Of The Lungs

Pneumonia is one of the most serious diseases which cause the most deaths in the world. Viruses, bacteria, and fungi can cause pneumonia. The infection from spreading to the lungs in the human body. In order to diagnose this infection, a chest x-ray is carried out. The doctor uses X-ray image in order to diagnose or monitor the treatment of states in which inflammation of the lungs. X-rays are also used in the diagnosis of diseases such as emphysema, lung cancer, cancer of the line, and pipe, and tuberculosis (tb). However, a diagnosis of pneumonia requiring medical experts to comment on its presence felt in the chest x-ray. For decades, the auto- diagnosis (CAD) systems have been used for the respiratory disease based on chest X-ray images. Deep learning allows machines can quickly extract and classify objects from a photo. Ilham, with the great success of deep learning, we use a deep learning approach to detection of pneumonia into the work. Convolutional neural network that was developed for this study is the inflammation of the lungs. Supervised learning is ANCHORED to the use of features and functions. In general, the data of 5826 images with the help of one of the Kaggle.com. The CNN training and testing, that is, an open set of data. In the proposed method, the high success rate of accurate classification is achieved.

scholarly journals FLANNEL  (Focal Loss bAsed Neural Network EnsembLe) for COVID-19 detection

2020 ◽  
Author(s):  
Zhi Qiao ◽  
Austin Bae ◽  
Lucas M Glass ◽  
Cao Xiao ◽  
Jimeng Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Class Imbalance ◽  
X Rays ◽  
Neural Network Ensemble ◽  
Imaging Data ◽  
Identification Task ◽  
Neural Network Models ◽  
X Ray ◽  
Chest X Ray

Abstract Objective The study sought to test the possibility of differentiating chest x-ray images of coronavirus disease 2019 (COVID-19) against other pneumonia and healthy patients using deep neural networks. Materials and Methods We construct the radiography (x-ray) imaging data from 2 publicly available sources, which include 5508 chest x-ray images across 2874 patients with 4 classes: normal, bacterial pneumonia, non–COVID-19 viral pneumonia, and COVID-19. To identify COVID-19, we propose a FLANNEL (Focal Loss bAsed Neural Network EnsembLe) model, a flexible module to ensemble several convolutional neural network models and fuse with a focal loss for accurate COVID-19 detection on class imbalance data. Results FLANNEL consistently outperforms baseline models on COVID-19 identification task in all metrics. Compared with the best baseline, FLANNEL shows a higher macro-F1 score, with 6% relative increase on the COVID-19 identification task, in which it achieves precision of 0.7833 ± 0.07, recall of 0.8609 ± 0.03, and F1 score of 0.8168 ± 0.03. Discussion Ensemble learning that combines multiple independent basis classifiers can increase the robustness and accuracy. We propose a neural weighing module to learn the importance weight for each base model and combine them via weighted ensemble to get the final classification results. In order to handle the class imbalance challenge, we adapt focal loss to our multiple classification task as the loss function. Conclusion FLANNEL effectively combines state-of-the-art convolutional neural network classification models and tackles class imbalance with focal loss to achieve better performance on COVID-19 detection from x-rays.

scholarly journals Attention-Based Transfer Learning for Efficient Pneumonia Detection in Chest X-ray Images

2021 ◽  
Vol 11 (3) ◽  
pp. 1242
Author(s):  
So-Mi Cha ◽  
Seung-Seok Lee ◽  
Bonggyun Ko
Keyword(s):  
Transfer Learning ◽  
Area Under The Curve ◽  
Attention Mechanism ◽  
Stable Model ◽  
X Rays ◽  
X Ray ◽  
Learning Framework ◽  
Computer Aided ◽  
Common Technique ◽  
Chest X Ray

Pneumonia is a form of acute respiratory infection commonly caused by germs, viruses, and fungi, and can prove fatal at any age. Chest X-rays is the most common technique for diagnosing pneumonia. There have been several attempts to apply transfer learning based on a Convolutional Neural Network to build a stable model in computer-aided diagnosis. Recently, with the appearance of an attention mechanism that automatically focuses on the critical part of the image that is crucial for the diagnosis of disease, it is possible to increase the performance of previous models. The goal of this study is to improve the accuracy of a computer-aided diagnostic approach that medical professionals can easily use as an auxiliary tool. In this paper, we proposed the attention-based transfer learning framework for efficient pneumonia detection in chest X-ray images. We collected features from three-types of pre-trained models, ResNet152, DenseNet121, ResNet18 as a role of feature extractor. We redefined the classifier for a new task and applied the attention mechanism as a feature selector. As a result, the proposed approach achieved accuracy, F-score, Area Under the Curve(AUC), precision and recall of 96.63%, 0.973, 96.03%, 96.23% and 98.46%, respectively.

scholarly journals Pneumonia detection in chest X-ray images using an ensemble of deep learning models

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256630
Author(s):  
Rohit Kundu ◽  
Ritacheta Das ◽  
Zong Woo Geem ◽  
Gi-Tae Han ◽  
Ram Sarkar
Keyword(s):  
Survival Rates ◽  
Area Under The Curve ◽  
Weighted Average ◽  
Weight Vector ◽  
X Rays ◽  
Neural Network Models ◽  
X Ray ◽  
Ensemble Technique ◽  
Ensemble Techniques ◽  
Chest X Ray

Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many individuals, especially in developing and underdeveloped nations, where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, together with inadequate medical infrastructure. Pneumonia causes pleural effusion, a condition in which fluids fill the lung, causing respiratory difficulty. Early diagnosis of pneumonia is crucial to ensure curative treatment and increase survival rates. Chest X-ray imaging is the most frequently used method for diagnosing pneumonia. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. In this study, we developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We employed deep transfer learning to handle the scarcity of available data and designed an ensemble of three convolutional neural network models: GoogLeNet, ResNet-18, and DenseNet-121. A weighted average ensemble technique was adopted, wherein the weights assigned to the base learners were determined using a novel approach. The scores of four standard evaluation metrics, precision, recall, f1-score, and the area under the curve, are fused to form the weight vector, which in studies in the literature was frequently set experimentally, a method that is prone to error. The proposed approach was evaluated on two publicly available pneumonia X-ray datasets, provided by Kermany et al. and the Radiological Society of North America (RSNA), respectively, using a five-fold cross-validation scheme. The proposed method achieved accuracy rates of 98.81% and 86.85% and sensitivity rates of 98.80% and 87.02% on the Kermany and RSNA datasets, respectively. The results were superior to those of state-of-the-art methods and our method performed better than the widely used ensemble techniques. Statistical analyses on the datasets using McNemar’s and ANOVA tests showed the robustness of the approach. The codes for the proposed work are available at https://github.com/Rohit-Kundu/Ensemble-Pneumonia-Detection.

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