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<p>In medical terminology, there are two types of tumors: benign and malignant neoplasms, with over 200 different types that can affect humans. It requires a considerable of time to identify a brain tumor, and the radiologist’s skill and experience are crucial. Traditional approaches are now both expensive and inefficient due to the substantial rise in The volume of data that needs to be processed because to the growing number of patients. Deep Learning (DL) techniques have become increasingly popular for developing computer algorithms capable of accurately and rapidly diagnosing or segmenting Brain Tumors (BT). Utilizing the U-Net architecture, this study generates an illustration of the probability that each pixel in the provided image is positioned inside a tumor region. U-NET Ensemble (UNETE) is a complicated technique for segmenting images intended for use in medical image analysis, capable of accurately segmenting images even with a limited training dataset. Efficient Net, Improved Multiscale Vision Transformer (MVITV2), and Multi-Axis Vision Transformer (MAXVIT) are the three backbone networks that the U-Net models employ. The final segmentation maps are produced using a loss function that integrates the varied features learned by each backbone network. Brain tumor classification is carried out using a Convolutional Neural Network (CNN) model with a fine-tuned ResNet50. The ResNet50 architecture is employed to detect brain tumors by identifying their presence in MRI scans. Deep learning methods for tumor identification are used to the Brain Tumor Segmentation 2019 (BraTS 2019) dataset. This dataset comprises four structural imaging modalities: T2 Weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR), transverse relaxation time (T2), longitudinal relaxation time (T1), and contrast-enhanced longitudinal relaxation time (T1c). The brain MRI scans of adult glioma patients comprise this dataset. It also provides manually annotated ground truth labels for different tumor sub-regions, including enhancement, necrosis, and edema, along with their MGMT promoter methylation status. Primacy, recall, F1-score, accuracy, loss, intersection over union (IoU), and dice similarity coefficient assess the model.</p>

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U-Net ensemble based segmentation and RESNET for brain tumor detection of magnetic resonance imaging (MRI)

  • 期刊论文

  • 发表日期：

    2025年9月12日

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  • S. Suganthi Rani,
  • Kavitha Subramanian,
  • Venkatatesan Rajinikanth,
  • Rajakumar Selvarajan

摘要

In medical terminology, there are two types of tumors: benign and malignant neoplasms, with over 200 different types that can affect humans. It requires a considerable of time to identify a brain tumor, and the radiologist’s skill and experience are crucial. Traditional approaches are now both expensive and inefficient due to the substantial rise in The volume of data that needs to be processed because to the growing number of patients. Deep Learning (DL) techniques have become increasingly popular for developing computer algorithms capable of accurately and rapidly diagnosing or segmenting Brain Tumors (BT). Utilizing the U-Net architecture, this study generates an illustration of the probability that each pixel in the provided image is positioned inside a tumor region. U-NET Ensemble (UNETE) is a complicated technique for segmenting images intended for use in medical image analysis, capable of accurately segmenting images even with a limited training dataset. Efficient Net, Improved Multiscale Vision Transformer (MVITV2), and Multi-Axis Vision Transformer (MAXVIT) are the three backbone networks that the U-Net models employ. The final segmentation maps are produced using a loss function that integrates the varied features learned by each backbone network. Brain tumor classification is carried out using a Convolutional Neural Network (CNN) model with a fine-tuned ResNet50. The ResNet50 architecture is employed to detect brain tumors by identifying their presence in MRI scans. Deep learning methods for tumor identification are used to the Brain Tumor Segmentation 2019 (BraTS 2019) dataset. This dataset comprises four structural imaging modalities: T2 Weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR), transverse relaxation time (T2), longitudinal relaxation time (T1), and contrast-enhanced longitudinal relaxation time (T1c). The brain MRI scans of adult glioma patients comprise this dataset. It also provides manually annotated ground truth labels for different tumor sub-regions, including enhancement, necrosis, and edema, along with their MGMT promoter methylation status. Primacy, recall, F1-score, accuracy, loss, intersection over union (IoU), and dice similarity coefficient assess the model.