BRAIN TUMOR DIAGNOSIS BASED ON MEDICAL IMAGES USING VISION TRANSFORMER
Brain tumor is one of the most common causes of death in modern times. Early and accurate detection of this disease can save the lives of a large part of the world’s population. Accurate diagnosis and classification of brain tumors in patients using machine learning and deep learning is of grea...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Information Technology Publishing House
2025-07-01
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| Series: | Problems of Information Society |
| Online Access: | https://jpis.az/uploads/article/en/2025_2/BRAIN_TUMOR_DIAGNOSIS_BASED_ON_MEDICAL_IMAGES_USING_VISION_TRANSFORMER.pdf |
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| Summary: | Brain tumor is one of the most common causes of death in modern times. Early and
accurate detection of this disease can save the lives of a large part of the world’s
population. Accurate diagnosis and classification of brain tumors in patients using
machine learning and deep learning is of great importance in determining effective
treatment methods. This article develops the models based on vision transformer,
multi-block convolutional neural networks and k-nearest-neighbors, which provide
high-precision detection and categorization of brain tumors in patients using magnetic
resonance imaging. The main advantage of applying these models is that the processes
of feature extraction in images are implemented through attention and filtration
mechanisms, rather than the traditional segmentation methods. The proposed models
are tested on the Brain Tumor MRI database containing 7023 histological images open
for scientific research and evaluated based on various metrics. Comparative analysis of
the evaluation results determines a model that identifies all images containing
pathological changes with higher accuracy. |
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| ISSN: | 2077-964X 2309-7566 |