Improving Brain Tumor Detection with Deep Learning Models: A Comparative Analysis Using MRI and RO-SVM Classification

Cafer Aslım; Abidin Çalışkan

Makale detayları

Improving Brain Tumor Detection with Deep Learning Models: A Comparative Analysis Using MRI and RO-SVM Classification

Yazarlar:

Cafer Aslım | Abidin Çalışkan

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 2, pp.647-663

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351267

pISSN:

2147-3129

Anahtar kelimeler:

Özet:

 Brain tumors are one of the most important health problems that threaten human life. Therefore, accurate diagnosis at an early stage is vital. Magnetic resonance imaging MRI is one of the most effective and important methods for detecting brain tumors. It is thought that instead of disease detection using traditional methods, artificial intelligence-based computer applications can make significant contributions to experts in detecting brain tumors. Deep learning DL models, which are very popular in the scientific world today, are used extensively in the processing of images obtained in the field of health and in the detection of diseases. In this study, VGG-19, ResNet-101 and DenseNet-121 DS models trained on the ImageNet dataset were used to detect brain tumors with magnetic resonance MR images. The MR images used in the study were pre-processed and the excess images were identified and cropped to obtain high efficiency. After the image ratios were adjusted, random forest RF and support vector machines SVM classification algorithms were used. In the experimental studies, the highest accuracy values were achieved with DenseNet-121 using RF and SVM classification algorithms in detecting brain tumors with the proposed method. Results of 87.67 were obtained with SVM and 85.83 with RO.

 Brain tumors are one of the most important health problems that threaten human life. Therefore, accurate diagnosis at an early stage is vital. Magnetic resonance imaging MRI is one of the most effective and important methods for detecting brain tumors. It is thought that instead of disease detection using traditional methods, artificial intelligence-based computer applications can make significant contributions to experts in detecting brain tumors. Deep learning DL models, which are very popular in the scientific world today, are used extensively in the processing of images obtained in the field of health and in the detection of diseases. In this study, VGG-19, ResNet-101 and DenseNet-121 DS models trained on the ImageNet dataset were used to detect brain tumors with magnetic resonance MR images. The MR images used in the study were pre-processed and the excess images were identified and cropped to obtain high efficiency. After the image ratios were adjusted, random forest RF and support vector machines SVM classification algorithms were used. In the experimental studies, the highest accuracy values were achieved with DenseNet-121 using RF and SVM classification algorithms in detecting brain tumors with the proposed method. Results of 87.67 were obtained with SVM and 85.83 with RO.

 Brain tumors are one of the most important health problems that threaten human life. Therefore, accurate diagnosis at an early stage is vital. Magnetic resonance imaging MRI is one of the most effective and important methods for detecting brain tumors. It is thought that instead of disease detection using traditional methods, artificial intelligence-based computer applications can make significant contributions to experts in detecting brain tumors. Deep learning DL models, which are very popular in the scientific world today, are used extensively in the processing of images obtained in the field of health and in the detection of diseases. In this study, VGG-19, ResNet-101 and DenseNet-121 DS models trained on the ImageNet dataset were used to detect brain tumors with magnetic resonance MR images. The MR images used in the study were pre-processed and the excess images were identified and cropped to obtain high efficiency. After the image ratios were adjusted, random forest RF and support vector machines SVM classification algorithms were used. In the experimental studies, the highest accuracy values were achieved with DenseNet-121 using RF and SVM classification algorithms in detecting brain tumors with the proposed method. Results of 87.67 were obtained with SVM and 85.83 with RO.

 Brain tumors are one of the most important health problems that threaten human life. Therefore, accurate diagnosis at an early stage is vital. Magnetic resonance imaging MRI is one of the most effective and important methods for detecting brain tumors. It is thought that instead of disease detection using traditional methods, artificial intelligence-based computer applications can make significant contributions to experts in detecting brain tumors. Deep learning DL models, which are very popular in the scientific world today, are used extensively in the processing of images obtained in the field of health and in the detection of diseases. In this study, VGG-19, ResNet-101 and DenseNet-121 DS models trained on the ImageNet dataset were used to detect brain tumors with magnetic resonance MR images. The MR images used in the study were pre-processed and the excess images were identified and cropped to obtain high efficiency. After the image ratios were adjusted, random forest RF and support vector machines SVM classification algorithms were used. In the experimental studies, the highest accuracy values were achieved with DenseNet-121 using RF and SVM classification algorithms in detecting brain tumors with the proposed method. Results of 87.67 were obtained with SVM and 85.83 with RO.

Makale detayları

Improving Brain Tumor Detection with Deep Learning Models: A Comparative Analysis Using MRI and RO-SVM Classification

Yazarlar:

Cafer Aslım | Abidin Çalışkan

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 2, pp.647-663

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351267

pISSN:

2147-3129

Brain Tumor

VGG-19

ResNet-101

DenseNet-121

Random Forest

Support Vector Machines

Sayı

Makale detayları

Improving Brain Tumor Detection with Deep Learning Models: A Comparative Analysis Using MRI and RO-SVM Classification

Yazarlar: Cafer Aslım | Abidin Çalışkan

Yayıncı: Bitlis Eren University

Ana Başlıklar: Artificial Intelligence (Other)

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 2, pp.647-663

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001351267

pISSN: 2147-3129

Brain Tumor

VGG-19

ResNet-101

DenseNet-121

Random Forest

Support Vector Machines

Sayı

Yazarlar:

Cafer Aslım | Abidin Çalışkan

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 2, pp.647-663

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351267

pISSN:

2147-3129