Brain tumor classification using deep convolutional neural networks
This study presents a comparative analysis of various convolutional neural network (CNN) models for brain tumor detection on MRI medical images. The primary aim was to assess the effectiveness of different CNN architectures in accurately identifying brain tumors. Multiple models were trained, includ...
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| Format: | Article |
| Language: | English |
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Samara National Research University
2025-04-01
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| Series: | Компьютерная оптика |
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| Online Access: | https://computeroptics.ru/KO/Annot/KO49-2/490210.html |
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| author | M. Nurtay M. Kissina A. Tau A. Akhmetov G. Alina N. Mutovina |
| author_facet | M. Nurtay M. Kissina A. Tau A. Akhmetov G. Alina N. Mutovina |
| author_sort | M. Nurtay |
| collection | DOAJ |
| description | This study presents a comparative analysis of various convolutional neural network (CNN) models for brain tumor detection on MRI medical images. The primary aim was to assess the effectiveness of different CNN architectures in accurately identifying brain tumors. Multiple models were trained, including a custom-designed CNN with its specific layer architecture, and models based on Transfer Learning utilizing pre-trained neural networks: ResNet-50, VGG-16, and Xception. Performance evaluation of each model in terms of accuracy metrics such as precision, recall, F1-score, and confusion matrix on a test dataset was carried out. The dataset used in this study was obtained from the openly accessible Kaggle competition "Brain Tumor Detection from MRI." This dataset consisted of four classes: glioma, meningioma, no tumor (healthy), and pituitary, ensuring a balanced representation. Testing four models revealed that the custom CNN architecture, utilizing separable convolutions and batch normalization, achieved an average ROC AUC score of 0.99, outperforming the other models. Moreover, this model demonstrated an accuracy of 0.94, indicating its robust performance in brain tumor classification on MRI images. |
| format | Article |
| id | doaj-art-cffb4d61dd0c4854934fba409bcf29ba |
| institution | Kabale University |
| issn | 0134-2452 2412-6179 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj-art-cffb4d61dd0c4854934fba409bcf29ba2025-08-20T17:20:18ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792025-04-0149225326210.18287/2412-6179-CO-1476Brain tumor classification using deep convolutional neural networksM. Nurtay0M. Kissina1A. Tau2A. Akhmetov3G. Alina4N. Mutovina5Abylkas Saginov Karagandy Technical UniversityAbylkas Saginov Karagandy Technical UniversityAbylkas Saginov Karagandy Technical UniversityAbylkas Saginov Karagandy Technical UniversityAbylkas Saginov Karagandy Technical UniversityAbylkas Saginov Karagandy Technical UniversityThis study presents a comparative analysis of various convolutional neural network (CNN) models for brain tumor detection on MRI medical images. The primary aim was to assess the effectiveness of different CNN architectures in accurately identifying brain tumors. Multiple models were trained, including a custom-designed CNN with its specific layer architecture, and models based on Transfer Learning utilizing pre-trained neural networks: ResNet-50, VGG-16, and Xception. Performance evaluation of each model in terms of accuracy metrics such as precision, recall, F1-score, and confusion matrix on a test dataset was carried out. The dataset used in this study was obtained from the openly accessible Kaggle competition "Brain Tumor Detection from MRI." This dataset consisted of four classes: glioma, meningioma, no tumor (healthy), and pituitary, ensuring a balanced representation. Testing four models revealed that the custom CNN architecture, utilizing separable convolutions and batch normalization, achieved an average ROC AUC score of 0.99, outperforming the other models. Moreover, this model demonstrated an accuracy of 0.94, indicating its robust performance in brain tumor classification on MRI images.https://computeroptics.ru/KO/Annot/KO49-2/490210.htmlbrain tumorcomputer visionpattern recognitionmachine learningdeep learningconvolutional neural networktransfer learning |
| spellingShingle | M. Nurtay M. Kissina A. Tau A. Akhmetov G. Alina N. Mutovina Brain tumor classification using deep convolutional neural networks Компьютерная оптика brain tumor computer vision pattern recognition machine learning deep learning convolutional neural network transfer learning |
| title | Brain tumor classification using deep convolutional neural networks |
| title_full | Brain tumor classification using deep convolutional neural networks |
| title_fullStr | Brain tumor classification using deep convolutional neural networks |
| title_full_unstemmed | Brain tumor classification using deep convolutional neural networks |
| title_short | Brain tumor classification using deep convolutional neural networks |
| title_sort | brain tumor classification using deep convolutional neural networks |
| topic | brain tumor computer vision pattern recognition machine learning deep learning convolutional neural network transfer learning |
| url | https://computeroptics.ru/KO/Annot/KO49-2/490210.html |
| work_keys_str_mv | AT mnurtay braintumorclassificationusingdeepconvolutionalneuralnetworks AT mkissina braintumorclassificationusingdeepconvolutionalneuralnetworks AT atau braintumorclassificationusingdeepconvolutionalneuralnetworks AT aakhmetov braintumorclassificationusingdeepconvolutionalneuralnetworks AT galina braintumorclassificationusingdeepconvolutionalneuralnetworks AT nmutovina braintumorclassificationusingdeepconvolutionalneuralnetworks |