Deep-Learning based Automated Detection of Pneumonia in Chest Radiographs

Arunmozhi, S.; Rajinikanth, V.; Rajakumar, M. P.

doi:10.1109/icscan53069.2021.9526482

Cited by 10 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detection of lung infection using chest X-ray is a commonly approved technique and, several previous works can be found in the literature using conventional machine learning and advanced deep learning to detect the virus cases [1,3,23]. COVID-19 is a deadly disease, which causes severe lung infection.…”

Section: Contextmentioning

confidence: 99%

“…The proposed work tests the value of the developed structure using DLM first, then the features of the DLM and MLM are optimized with FA. After reducing the feature vector to a lower level, a serial concatenation is then implemented to get a Alyasseri et al [3] This research demonstrated a detailed review on the detection of COVID-19 with various machine and deep learning techniques. Houssein et al [8] Implementation of improved Convolutional Neural Network (CNN) technique is presented to diagnose the infection from X-ray pictures.…”

Section: Contextmentioning

confidence: 99%

“…The rapid development in modern life-saving facilities helped the human community to get a better medical facility through multi-speciality hospitals. Even though sufficient diagnostic and treatment facilities are easily accessible, the happening rate of life-threatening diseases is steadily rising [1,3,5,6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Kannan

Sivakumar

Ezhilarasi

2022

XST

View full text Add to dashboard Cite

Since the infectious disease occurrence rate in the human community is gradually rising due to varied reasons, appropriate diagnosis and treatments are essential to control its spread. The recently discovered COVID-19 is one of the contagious diseases, which infected numerous people globally. This contagious disease is arrested by several diagnoses and handling actions. Medical image-supported diagnosis of COVID-19 infection is an approved clinical practice. This research aims to develop a new Deep Learning Method (DLM) to detect the COVID-19 infection using the chest X-ray. The proposed work implemented two methods namely, detection of COVID-19 infection using (i) a Firefly Algorithm (FA) optimized deep-features and (ii) the combined deep and machine features optimized with FA. In this work, a 5-fold cross-validation method is engaged to train and test detection methods. The performance of this system is analyzed individually resulting in the confirmation that the deep feature-based technique helps to achieve a detection accuracy of > 92% with SVM-RBF classifier and combining deep and machine features achieves > 96% accuracy with Fine KNN classifier. In the future, this technique may have potential to play a vital role in testing and validating the X-ray images collected from patients suffering from the infection diseases.

show abstract

Section: Contextmentioning

confidence: 99%

Section: Contextmentioning

confidence: 99%

See 1 more Smart Citation

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Kannan

Sivakumar

Ezhilarasi

2022

XST

View full text Add to dashboard Cite

show abstract

“…Pneumonia, caused by microorganisms, is a severely contagious disease that damages one or both the lungs of the patients 23 . Early detection and treatment are typically favoured to recover infected patients since untreated pneumonia can lead to major complications in the older (>65 years) and children (<5 years) 3 Every year, the disease claims the lives of 700,000 kids and affects 7% of the global population 4 . The World Health Organisation (WHO) states that there are many degrees of pneumonia, from mild to severe, extremely risky, and potentially fatal.…”

Section: Introductionmentioning

confidence: 99%

“…The World Health Organisation (WHO) states that there are many degrees of pneumonia, from mild to severe, extremely risky, and potentially fatal. Early detection and effective treatment are therefore often preferred to help infected patients recover [3]. There are 4 distinct forms of pneumonia: 1) Community-Acquired Pneumonia (CAP) -pneumonia contracted in a community environment, outside the hospital 5 ; 2) Hospital-Acquired Pneumonia (HAP)pneumonia that was contracted ≥48 h after being admitted to an inpatient facility, such as a hospital; 3) Ventilator-Associated Pneumonia (VAP) 6 -this form of pneumonia is noted in patients who were mechanically ventilated using a ventilator for ≥48 h. 30% of the VAP cases take place in the Intensive Care Units (ICUs) 7 ;4) Aspiration pneumoniaarises from the aspirated microbes from the oropharynx or stomach 8 .…”

Section: Introductionmentioning

confidence: 99%

Pneumonia Detection Using enhanced CNN Model on Chest X-Ray Images

Aljawarneh

Al-Quraan

2022

MRAJ

View full text Add to dashboard Cite

Objective: Pneumonia, caused by microorganisms, is a severely contagious disease that damages one or both the lungs of the patients. Early detection and treatment are typically favoured to recover infected patients since untreated pneumonia can lead to major complications in the older (>65 years) and children (<5 years). The objectives of this work are to develop several models to evaluate X-ray Images (XRIs) of the chest, determine whether the images show/ do not show signs of pneumonia and compare the models based on their accuracy, precision, recall, loss, and ROC AUC scores. Methods: CNN, VGG-19, ResNet-50, and ResNet-50 with fine-tuning are some of the deep learning algorithms employed in this study. By training the transfer learning model and upgraded CNN model using a dataset, these techniques are used to identify pneumonia. The data set for the study was obtained from Kaggle. This dataset included 5,863 chest XRIs, which were categorised into 3 different folders (i.e., train, val, test). Results: According to the experimental findings, the ResNet50 model showed the lowest accuracy, i.e., 82.8%; while the upgraded CNN model showed the highest accuracy of 92.4%. Owing to its high accuracy, CNN was regarded as the best model in this study. The techniques developed in this study outperformed the popular ensemble techniques, and the models showed better results than those generated by cutting-edge methods. Deep learning models can detect the progression of pneumonia, which improves the general diagnostic accuracy and gives patients new hope for speedy treatment. Conclusions: Since enhanced CNN and ResNet50 showed the highest accuracy compared to other algorithms, it was concluded that these techniques could be effectively used to identify pneumonia after performing fine-tuning. It should be noted that the improved CNN would be more helpful in diagnosing pneumonia than the conventional method, which involves the healthcare provider reviewing the patient's medical history, performing a physical exam, and ordering diagnostic procedures like a chest X-ray. This information helps in identifying the form of pneumonia affecting the patient. Furthermore, pneumonia can take a severe turn and become fatal.

show abstract

Automated Detection of Multi Class Lung Diseases Using Deep Learning with the Help of X-ray Chest Images

Itagi²

2023

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

Deep-Learning based Automated Detection of Pneumonia in Chest Radiographs

Cited by 10 publications

References 12 publications

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Pneumonia Detection Using enhanced CNN Model on Chest X-Ray Images

Automated Detection of Multi Class Lung Diseases Using Deep Learning with the Help of X-ray Chest Images

Contact Info

Product

Resources

About