scholarly journals Pneumonia Classification using Deep Learning in Healthcare

2020 ◽  
Vol 9 (4) ◽  
pp. 1715-1723 ◽  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Research Work ◽  
Neural System ◽  
X Ray ◽  
Python Language ◽  
Learning Techniques ◽  
Improved Outcomes ◽  
Augmentation Techniques ◽  
Chest X Ray

There is a great growing interest in the domain of deep learning techniques for identifying and classifying images with various datasets. An enormous availability of datasets (e.g. ChestX-Ray14 dataset) has developed a keen interest in deep learning. Pneumonia is a disease that is caused by various bacteria, virus etc. X-ray is one of the major diagnosis tools for diagnosing pneumonia. This research work mainly proposes a convolutional neural system (CNN) model prepared without any preparation to group and identify the occurrence of pneumonia disease from a given assortment of chest X-ray image tests. Dissimilar to different strategies that depend exclusively on more learning draws near or conventional carefully assembled systems to accomplish an amazing grouping execution, and developed a convolutional neural arrange model without any preparation to separate and character the images to decide whether an individual is suffering with pneumonia. This model could help alleviate the dependability and difficult challenges frequently confronted to manage therapeutic problems. In this paper, CNN algorithm has been used along with different data augmentation techniques for improving the classification accuracies which has been discussed to increase the performance which will help in improving the validation and training accuracies and characterization of exactness of the CNN model and accomplished various results. This experiment was carried out using python language and has shown improved outcomes.

scholarly journals Deep learning for COVID-19 diagnosis based on chest X-ray images

2021 ◽  
Vol 11 (5) ◽  
pp. 4531
Author(s):  
Nashat Alrefai ◽  
Othman Ibrahim
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Promising Result ◽  
Test Accuracy ◽  
Training Time ◽  
X Ray ◽  
Global Pandemic ◽  
Augmentation Techniques ◽  
Chest X Ray ◽  
Using Data

Coronavirus disease 2019 (COVID-19) is a recent global pandemic that has affected many countries around the world, causing serious health problems, especially in the lungs. Although temperature testing is suggested as a firstline test for COVID-19, it was not reliable because many diseases have the same symptoms. Thus, we propose a deep learning method based on X-ray images that used a convolutional neural network (CNN) and transfer learning (TL) for COVID-19 diagnosis, and using gradient-weighted class activation mapping (Grad-CAM) technique for producing visual explanations for the COVID-19 infection area in the lung. The low sample size of coronavirus samples was considered a challenge, thus, this issue was overridden using data augmentation techniques. The study found that the proposed (CNN) and the modified pre-trained networks VGG16 and InceptionV3 achieved a promising result for COVID-19 diagnosis by using chest X-ray images. The proposed CNN was able to differentiate 284 patients with COVID-19 or normal with 98.2 percent for training accuracy and 96.66 percent for test accuracy and 100.0 percent sensitivity. The modified VGG16 achieved the best classification result between all with 100.0 percent for training accuracy and 98.33 percent for test accuracy and 100.0 percent sensitivity, but the proposed CNN overcame the others in the side of reducing the computational complexity and training time significantly.

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 Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

2022 ◽  
Vol 70 (1) ◽  
pp. 1541-1556
Author(s):  
Shubham Mahajan ◽  
Akshay Raina ◽  
Mohamed Abouhawwash ◽  
Xiao-Zhi Gao ◽  
Amit Kant Pandit
Keyword(s):  
Deep Learning ◽  
X Ray ◽  
Learning Techniques ◽  
Chest X Ray

scholarly journals Automatic Detection of COVID-19 and Pneumonia from Chest X-Ray using Transfer Learning

2020 ◽  
Author(s):  
Sarath Pathari ◽  
Rahul U
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Clinical Picture ◽  
Bacterial Pneumonia ◽  
Automatic Detection ◽  
Neural System ◽  
Cutting Edge ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray

In this study, a dataset of X-ray images from patients with common viral pneumonia, bacterial pneumonia, confirmed Covid-19 disease was utilized for the automatic detection of the Coronavirus disease. The point of the investigation is to assess the exhibition of cutting edge convolutional neural system structures proposed over the ongoing years for clinical picture order. In particular, the system called Transfer Learning was received. With transfer learning, the location of different variations from the norm in little clinical picture datasets is a reachable objective, regularly yielding amazing outcomes. The datasets used in this trial. Firstly, a collection of 24000 X-ray images includes 6000 images for confirmed Covid-19 disease,6000 confirmed common bacterial pneumonia and 6000 images of normal conditions. The information was gathered and expanded from the accessible X-Ray pictures on open clinical stores. The outcomes recommend that Deep Learning with X-Ray imaging may separate noteworthy biomarkers identified with the Covid-19 sickness, while the best precision, affectability, and particularity acquired is 97.83%, 96.81%, and 98.56% individually.

Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography

2020 ◽  
Vol 77 (9) ◽  
pp. 597-602
Author(s):  
Xiaohua Wang ◽  
Juezhao Yu ◽  
Qiao Zhu ◽  
Shuqiang Li ◽  
Zanmei Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Classification Performance ◽  
X Ray ◽  
Testing Dataset ◽  
Learning Techniques ◽  
History Of ◽  
Digital Chest ◽  
Chest X Ray

ObjectivesTo investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.MethodsWe retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, we applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC). In addition, we asked two certified radiologists to independently interpret the images in the testing dataset and compared their performance with the computerised scheme.ResultsThe Inception-V3 CNN architecture, which was trained on the combination of the three image sets, achieved an AUC of 0.878 (95% CI 0.811 to 0.946). The performance of the two radiologists in terms of AUC was 0.668 (95% CI 0.555 to 0.782) and 0.772 (95% CI 0.677 to 0.866), respectively. The agreement between the two readers was moderate (kappa: 0.423, p<0.001).ConclusionOur experimental results demonstrated that the deep leaning solution could achieve a relatively better performance in classification as compared with other models and the certified radiologists, suggesting the feasibility of deep learning techniques in screening pneumoconiosis.

scholarly journals Evaluation of Deep Convolutional Generative Adversarial Networks for Data Augmentation of Chest X-ray Images

2020 ◽  
Vol 13 (1) ◽  
pp. 8
Author(s):  
Sagar Kora Venu ◽  
Sridhar Ravula
Keyword(s):  
Data Augmentation ◽  
Medical Condition ◽  
Training Data ◽  
Superior Performance ◽  
Generative Adversarial Networks ◽  
X Rays ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Augmentation Techniques ◽  
Chest X Ray

Medical image datasets are usually imbalanced due to the high costs of obtaining the data and time-consuming annotations. Training a deep neural network model on such datasets to accurately classify the medical condition does not yield the desired results as they often over-fit the majority class samples’ data. Data augmentation is often performed on the training data to address the issue by position augmentation techniques such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue to increase the dataset sizes. Radiologists generally use chest X-rays for the diagnosis of pneumonia. Due to patient privacy concerns, access to such data is often protected. In this study, we performed data augmentation on the Chest X-ray dataset to generate artificial chest X-ray images of the under-represented class through generative modeling techniques such as the Deep Convolutional Generative Adversarial Network (DCGAN). With just 1341 chest X-ray images labeled as Normal, artificial samples were created by retaining similar characteristics to the original data with this technique. Evaluating the model resulted in a Fréchet Distance of Inception (FID) score of 1.289. We further show the superior performance of a CNN classifier trained on the DCGAN augmented dataset.

scholarly journals Diagnosing heart failure from chest X-ray images using deep learning

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Matsumoto ◽  
S Kodera ◽  
H Shinohara ◽  
A Kiyosue ◽  
Y Higashikuni ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Data Augmentation ◽  
Learning Algorithm ◽  
Funding Source ◽  
X Rays ◽  
X Ray ◽  
Cardiothoracic Ratio ◽  
Prediction Probability ◽  
Chest X Ray

Abstract   The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules and cardiomegaly in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this study, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists respectively verified and relabeled these images, for a total of 260 “normal” and 378 “heart failure” images, and the remainder were discarded because they had been incorrectly labeled. In this study “heart failure” was defined as “cardiomegaly or congestion”, in a chest X-ray with cardiothoracic ratio (CTR) over 50% or radiographic presence of pulmonary edema. To enable the machine to extract a sufficient number of features from the images, we used the general machine learning approach called data augmentation and transfer learning. Owing mostly to this technique and the adequate relabeling process, we established a model to detect heart failure in chest X-ray by applying deep learning, and obtained an accuracy of 82%. Sensitivity and specificity to heart failure were 75% and 94.4%, respectively. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. The figure shows randomly selected examples of the prediction probabilities and heatmaps of the chest X-rays from the dataset. The original image is on the left and its heatmap is on the right, with its prediction probability written below. The red areas on the heatmaps show important regions, according to which the machine determined the classification. While some images with ambiguous radiolucency such as (e) and (f) were prone to be misdiagnosed by this model, most of the images like (a)–(d) were diagnosed correctly. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images. Heatmaps and probabilities of prediction Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): JSPS KAKENHI

scholarly journals A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3482
Author(s):  
Abdullah-Al Nahid ◽  
Niloy Sikder ◽  
Anupam Kumar Bairagi ◽  
Md. Abdur Razzaque ◽  
Mehedi Masud ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Past Century ◽  
Radiographic Images ◽  
X Ray ◽  
The Past ◽  
Learning Techniques ◽  
Chest X Ray ◽  
Pneumonia Diagnosis

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient’s chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

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