Pneumonia detection in chest X-ray images using convolutional neural networks

Çağdaş Şimşek; Suat Özkorucuklu; Bora Işıldak

Article Details

Pneumonia detection in chest X-ray images using convolutional neural networks

Authors:

Çağdaş Şimşek | Suat Özkorucuklu | Bora Işıldak

Publisher:

Prusa Medical Publishing

Main Topics:

Deep Learning

Journal:

The European Research Journal Vol.11 Issue 5, pp.907-914

Document Type:

Article

Publication Date:

4/2025

Document Language:

English

Document ID (Org No.):

2001356981

pISSN:

2149-3189

Keywords:

Abstract:

 Objectives: Pneumonia ranks among the infections and presents a considerable health threat, especially in certain age groups and developing countries. The accurate diagnosis of the disease and prompt identification are crucial for treatment purposes. This study aimed to develope a convolutional deep neural network model that can detect pneumonia using a sufficient number of chest X-ray images that have been verified with a definite diagnosis clinically. Methods: This study uses a dataset that includes 1000 chest X-ray images from a variety of age groups taken as part of patient care at Koç University Faculty of Medicine Hospital Clinics. The dataset sample includes two sets of pictures called normal and pneumonia infected. Various preprocessing techniques were used on the obtained images, thus enabling the training and testing of our developed prediction model. Results: We improved the accuracy of the models decisions by applying image processing techniques, successfully achieving high levels of decision accuracy with our model We have elevated the precision of decision-making in our model to outstanding levels and achieved impressive F1 Score and AUC Area Under the Curve values F1 Score: 0.94 and AUC Score: 0.98. Conclusions: Our model was trained using X-ray images produced from the same devices of the same hospital and achieved very high prediction results, but using images produced from different countries, different hospitals and different devices, especially training and testing the model with much larger data sets, is a necessary need for this study and the model we developed to become more universal, and in this sense, there is a need to develop and expand the study.

 Objectives: Pneumonia ranks among the infections and presents a considerable health threat, especially in certain age groups and developing countries. The accurate diagnosis of the disease and prompt identification are crucial for treatment purposes. This study aimed to develope a convolutional deep neural network model that can detect pneumonia using a sufficient number of chest X-ray images that have been verified with a definite diagnosis clinically. Methods: This study uses a dataset that includes 1000 chest X-ray images from a variety of age groups taken as part of patient care at Koç University Faculty of Medicine Hospital Clinics. The dataset sample includes two sets of pictures called normal and pneumonia infected. Various preprocessing techniques were used on the obtained images, thus enabling the training and testing of our developed prediction model. Results: We improved the accuracy of the models decisions by applying image processing techniques, successfully achieving high levels of decision accuracy with our model We have elevated the precision of decision-making in our model to outstanding levels and achieved impressive F1 Score and AUC Area Under the Curve values F1 Score: 0.94 and AUC Score: 0.98. Conclusions: Our model was trained using X-ray images produced from the same devices of the same hospital and achieved very high prediction results, but using images produced from different countries, different hospitals and different devices, especially training and testing the model with much larger data sets, is a necessary need for this study and the model we developed to become more universal, and in this sense, there is a need to develop and expand the study.

 Objectives: Pneumonia ranks among the infections and presents a considerable health threat, especially in certain age groups and developing countries. The accurate diagnosis of the disease and prompt identification are crucial for treatment purposes. This study aimed to develope a convolutional deep neural network model that can detect pneumonia using a sufficient number of chest X-ray images that have been verified with a definite diagnosis clinically. Methods: This study uses a dataset that includes 1000 chest X-ray images from a variety of age groups taken as part of patient care at Koç University Faculty of Medicine Hospital Clinics. The dataset sample includes two sets of pictures called normal and pneumonia infected. Various preprocessing techniques were used on the obtained images, thus enabling the training and testing of our developed prediction model. Results: We improved the accuracy of the models decisions by applying image processing techniques, successfully achieving high levels of decision accuracy with our model We have elevated the precision of decision-making in our model to outstanding levels and achieved impressive F1 Score and AUC Area Under the Curve values F1 Score: 0.94 and AUC Score: 0.98. Conclusions: Our model was trained using X-ray images produced from the same devices of the same hospital and achieved very high prediction results, but using images produced from different countries, different hospitals and different devices, especially training and testing the model with much larger data sets, is a necessary need for this study and the model we developed to become more universal, and in this sense, there is a need to develop and expand the study.

 Objectives: Pneumonia ranks among the infections and presents a considerable health threat, especially in certain age groups and developing countries. The accurate diagnosis of the disease and prompt identification are crucial for treatment purposes. This study aimed to develope a convolutional deep neural network model that can detect pneumonia using a sufficient number of chest X-ray images that have been verified with a definite diagnosis clinically. Methods: This study uses a dataset that includes 1000 chest X-ray images from a variety of age groups taken as part of patient care at Koç University Faculty of Medicine Hospital Clinics. The dataset sample includes two sets of pictures called normal and pneumonia infected. Various preprocessing techniques were used on the obtained images, thus enabling the training and testing of our developed prediction model. Results: We improved the accuracy of the models decisions by applying image processing techniques, successfully achieving high levels of decision accuracy with our model We have elevated the precision of decision-making in our model to outstanding levels and achieved impressive F1 Score and AUC Area Under the Curve values F1 Score: 0.94 and AUC Score: 0.98. Conclusions: Our model was trained using X-ray images produced from the same devices of the same hospital and achieved very high prediction results, but using images produced from different countries, different hospitals and different devices, especially training and testing the model with much larger data sets, is a necessary need for this study and the model we developed to become more universal, and in this sense, there is a need to develop and expand the study.

Article Details

Pneumonia detection in chest X-ray images using convolutional neural networks

Authors:

Çağdaş Şimşek | Suat Özkorucuklu | Bora Işıldak

Publisher:

Prusa Medical Publishing

Main Topics:

Deep Learning

Journal:

The European Research Journal Vol.11 Issue 5, pp.907-914

Document Type:

Article

Publication Date:

4/2025

Document Language:

English

Document ID (Org No.):

2001356981

pISSN:

2149-3189

Pneumonia

convolutional neural network

chest X-ray

deep neural network

machine learning

deep learning

artificial intelligence

Issues

Article Details

Authors: Çağdaş Şimşek | Suat Özkorucuklu | Bora Işıldak

Publisher: Prusa Medical Publishing

Main Topics: Deep Learning

Journal: The European Research Journal Vol.11 Issue 5, pp.907-914

Document Type: Article

Publication Date: 4/2025

Document Language: English

Document ID (Org No.): 2001356981

pISSN: 2149-3189

Issues

Authors:

Çağdaş Şimşek | Suat Özkorucuklu | Bora Işıldak

Publisher:

Prusa Medical Publishing

Main Topics:

Deep Learning

Journal:

The European Research Journal Vol.11 Issue 5, pp.907-914

Document Type:

Article

Publication Date:

4/2025

Document Language:

English

Document ID (Org No.):

2001356981

pISSN:

2149-3189