PDF(Pubmed)
Abstract：
Brain tumors occur due to the expansion of abnormal cell tissues and can be malignant (cancerous) or benign (not cancerous). Numerous factors such as the position, size, and progression rate are considered while detecting and diagnosing brain tumors. Detecting brain tumors in their initial phases is vital for diagnosis where MRI (magnetic resonance imaging) scans play an important role. Over the years, deep learning models have been extensively used for medical image processing. The current study primarily investigates the novel Fine-Tuned Vision Transformer models (FTVTs)-FTVT-b16, FTVT-b32, FTVT-l16, FTVT-l32-for brain tumor classification, while also comparing them with other established deep learning models such as ResNet50, MobileNet-V2, and EfficientNet - B0. A dataset with 7,023 images (MRI scans) categorized into four different classes, namely, glioma, meningioma, pituitary, and no tumor are used for classification. Further, the study presents a comparative analysis of these models including their accuracies and other evaluation metrics including recall, precision, and F1-score across each class. The deep learning models ResNet-50, EfficientNet-B0, and MobileNet-V2 obtained an accuracy of 96.5%, 95.1%, and 94.9%, respectively. Among all the FTVT models, FTVT-l16 model achieved a remarkable accuracy of 98.70% whereas other FTVT models FTVT-b16, FTVT-b32, and FTVT-132 achieved an accuracy of 98.09%, 96.87%, 98.62%, respectively, hence proving the efficacy and robustness of FTVT\'s in medical image processing.
摘要：
脑肿瘤是由于异常细胞组织的扩张而发生的，可以是恶性的（癌性的）或良性的（非癌性的）。位置等众多因素，尺寸,在检测和诊断脑肿瘤时考虑进展率。在初始阶段检测脑肿瘤对于MRI（磁共振成像）扫描起着重要作用的诊断至关重要。多年来,深度学习模型已被广泛用于医学图像处理。目前的研究主要调查了新颖的微调视觉变换器模型（FTVT）-FTVT-b16，FTVT-b32，FTVT-l16，FTVT-l32-用于脑肿瘤分类，同时还将它们与其他已建立的深度学习模型进行比较，例如ResNet50、MobileNet-V2和EfficientNet-B0。包含7,023张图像（MRI扫描）的数据集分为四个不同的类别，即,神经胶质瘤,脑膜瘤,垂体,并且没有肿瘤用于分类。Further,该研究对这些模型进行了比较分析，包括它们的准确性和其他评估指标，包括召回，精度,每个班级的F1得分。深度学习模型ResNet-50、EfficientNet-B0和MobileNet-V2的准确率为96.5%，95.1%,94.9%,分别。在所有的FTVT模型中，FTVT-l16模型取得了98.70%的显著精度，而其他FTVT-b16、FTVT-b32和FTVT-132模型取得了98.09%的精度，96.87%,98.62%,分别,从而证明了FTVT在医学图像处理中的有效性和鲁棒性。
