Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach

Abdullah Şener; Burhan Ergen

Makale detayları

Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach

Yazarlar:

Abdullah Şener | Burhan Ergen

Yayıncı:

Sakarya University

Ana Başlıklar:

Software Engineering (Other)

Dergi:

Sakarya University Journal of Science Vol.27 Issue 5, pp.1128-1140

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001300456

pISSN:

2147-835X

Anahtar kelimeler:

Özet:

 Early detection and diagnosis of brain tumors have a critical impact on the treatment of brain tumor patients. This is because initiating interventions early directly impacts the patients chances of continuing their life. In the field of medical research, various methods are employed for the detection of brain tumors. Among these methods, magnetic resonance imaging MRI is the most popular due to its superior image quality. By leveraging technological advancements, the utilization of deep learning techniques in the identification of brain tumors ensures both high accuracy and simplification of the process. In a conducted study, a new model was developed by utilizing the VGG-19 architecture, a popular convolutional neural network model, to achieve high accuracy in brain tumor detection. In the study, precision, F1 score, accuracy, specificity, Matthews correlation coefficient, and recall metrics were used to evaluate the performance of the developed model. The deep learning model developed for brain tumor detection was trained and evaluated on an open-source dataset consisting of MRI images of gliomas, meningiomas, pituitary tumors, and healthy brains. The results obtained from the study demonstrate the promising potential of using the developed model in clinical applications for brain tumor detection. The high accuracy achieved by the developed model emphasizes its potential as an auxiliary resource for healthcare professionals in brain tumor detection. This research aims to evaluate the model as a valuable tool that can assist physicians in making informed treatment decisions regarding brain tumor diagnosis.

 Early detection and diagnosis of brain tumors have a critical impact on the treatment of brain tumor patients. This is because initiating interventions early directly impacts the patients chances of continuing their life. In the field of medical research, various methods are employed for the detection of brain tumors. Among these methods, magnetic resonance imaging MRI is the most popular due to its superior image quality. By leveraging technological advancements, the utilization of deep learning techniques in the identification of brain tumors ensures both high accuracy and simplification of the process. In a conducted study, a new model was developed by utilizing the VGG-19 architecture, a popular convolutional neural network model, to achieve high accuracy in brain tumor detection. In the study, precision, F1 score, accuracy, specificity, Matthews correlation coefficient, and recall metrics were used to evaluate the performance of the developed model. The deep learning model developed for brain tumor detection was trained and evaluated on an open-source dataset consisting of MRI images of gliomas, meningiomas, pituitary tumors, and healthy brains. The results obtained from the study demonstrate the promising potential of using the developed model in clinical applications for brain tumor detection. The high accuracy achieved by the developed model emphasizes its potential as an auxiliary resource for healthcare professionals in brain tumor detection. This research aims to evaluate the model as a valuable tool that can assist physicians in making informed treatment decisions regarding brain tumor diagnosis.

 Early detection and diagnosis of brain tumors have a critical impact on the treatment of brain tumor patients. This is because initiating interventions early directly impacts the patients chances of continuing their life. In the field of medical research, various methods are employed for the detection of brain tumors. Among these methods, magnetic resonance imaging MRI is the most popular due to its superior image quality. By leveraging technological advancements, the utilization of deep learning techniques in the identification of brain tumors ensures both high accuracy and simplification of the process. In a conducted study, a new model was developed by utilizing the VGG-19 architecture, a popular convolutional neural network model, to achieve high accuracy in brain tumor detection. In the study, precision, F1 score, accuracy, specificity, Matthews correlation coefficient, and recall metrics were used to evaluate the performance of the developed model. The deep learning model developed for brain tumor detection was trained and evaluated on an open-source dataset consisting of MRI images of gliomas, meningiomas, pituitary tumors, and healthy brains. The results obtained from the study demonstrate the promising potential of using the developed model in clinical applications for brain tumor detection. The high accuracy achieved by the developed model emphasizes its potential as an auxiliary resource for healthcare professionals in brain tumor detection. This research aims to evaluate the model as a valuable tool that can assist physicians in making informed treatment decisions regarding brain tumor diagnosis.

 Early detection and diagnosis of brain tumors have a critical impact on the treatment of brain tumor patients. This is because initiating interventions early directly impacts the patients chances of continuing their life. In the field of medical research, various methods are employed for the detection of brain tumors. Among these methods, magnetic resonance imaging MRI is the most popular due to its superior image quality. By leveraging technological advancements, the utilization of deep learning techniques in the identification of brain tumors ensures both high accuracy and simplification of the process. In a conducted study, a new model was developed by utilizing the VGG-19 architecture, a popular convolutional neural network model, to achieve high accuracy in brain tumor detection. In the study, precision, F1 score, accuracy, specificity, Matthews correlation coefficient, and recall metrics were used to evaluate the performance of the developed model. The deep learning model developed for brain tumor detection was trained and evaluated on an open-source dataset consisting of MRI images of gliomas, meningiomas, pituitary tumors, and healthy brains. The results obtained from the study demonstrate the promising potential of using the developed model in clinical applications for brain tumor detection. The high accuracy achieved by the developed model emphasizes its potential as an auxiliary resource for healthcare professionals in brain tumor detection. This research aims to evaluate the model as a valuable tool that can assist physicians in making informed treatment decisions regarding brain tumor diagnosis.

Makale detayları

Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach

Yazarlar:

Abdullah Şener | Burhan Ergen

Yayıncı:

Sakarya University

Ana Başlıklar:

Software Engineering (Other)

Dergi:

Sakarya University Journal of Science Vol.27 Issue 5, pp.1128-1140

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001300456

pISSN:

2147-835X

Brain tumor detection

image classification

VGG-19 architecture

deep learning

support vector machines.

Sayı

Makale detayları

Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach

Yazarlar: Abdullah Şener | Burhan Ergen

Yayıncı: Sakarya University

Ana Başlıklar: Software Engineering (Other)

Dergi: Sakarya University Journal of Science Vol.27 Issue 5, pp.1128-1140

Belge Tipi: Makale

Yayın Tarihi: 10/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001300456

pISSN: 2147-835X

Brain tumor detection

image classification

VGG-19 architecture

deep learning

support vector machines.

Sayı

Yazarlar:

Abdullah Şener | Burhan Ergen

Yayıncı:

Sakarya University

Ana Başlıklar:

Software Engineering (Other)

Dergi:

Sakarya University Journal of Science Vol.27 Issue 5, pp.1128-1140

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001300456

pISSN:

2147-835X