Allahem Hisham, El-Ghany Sameh Abd, Abd El-Aziz A A, Aldughayfiq Bader, Alshammeri Menwa, Alamri Malak
Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 42421, Saudi Arabia.
Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah 42421, Saudi Arabia.
Diagnostics (Basel). 2025 May 30;15(11):1392. doi: 10.3390/diagnostics15111392.
The brain serves as the central command center for the nervous system in the human body and is made up of nerve cells known as neurons. When these nerve cells grow rapidly and abnormally, it can lead to the development of a brain tumor. Brain tumors are severe conditions that can significantly reduce a person's lifespan. Failure to detect or delayed diagnosis of brain tumors can have fatal consequences. Accurately identifying and classifying brain tumors poses a considerable challenge for medical professionals, especially in terms of diagnosing and treating them using medical imaging analysis. Errors in diagnosing brain tumors can significantly impact a person's life expectancy. Magnetic Resonance Imaging (MRI) is highly effective in early detection, diagnosis, and classification of brain cancers due to its advanced imaging abilities for soft tissues. However, manual examination of brain MRI scans is prone to errors and heavily depends on radiologists' experience and fatigue levels. Swift detection of brain tumors is crucial for ensuring patient safety. In recent years, computer-aided diagnosis (CAD) systems incorporating deep learning (DL) and machine learning (ML) technologies have gained popularity as they offer precise predictive outcomes based on MRI images using advanced computer vision techniques. This article introduces a novel hybrid CAD approach named ViT-PCA-RF, which integrates Vision Transformer (ViT) and Principal Component Analysis (PCA) with Random Forest (RF) for brain tumor classification, providing a new method in the field. ViT was employed for feature extraction, PCA for feature dimension reduction, and RF for brain tumor classification. The proposed ViT-PCA-RF model helps detect early brain tumors, enabling timely intervention, better patient outcomes, and streamlining the diagnostic process, reducing patient time and costs. Our research trained and tested on the Brain Tumor MRI (BTM) dataset for multi-classification of brain tumors. The BTM dataset was preprocessed using resizing and normalization methods to ensure consistent input. Subsequently, our innovative model was compared against traditional classifiers, showcasing impressive performance metrics. It exhibited outstanding accuracy, specificity, precision, recall, and F1 score with rates of 99%, 99.4%, 98.1%, 98.1%, and 98.1%, respectively. : Our innovative classifier's evaluation underlined our model's potential, which leverages ViT, PCA, and RF techniques, showing promise in the precise and effective detection of brain tumors.
大脑是人体神经系统的中央指挥中心，由称为神经元的神经细胞组成。当这些神经细胞快速且异常生长时，可能会导致脑瘤的形成。脑瘤是严重的病症，会显著缩短人的寿命。未能检测到或延迟诊断脑瘤可能会带来致命后果。准确识别和分类脑瘤对医学专业人员构成了相当大的挑战，尤其是在使用医学影像分析进行诊断和治疗方面。脑瘤诊断错误会显著影响人的预期寿命。磁共振成像（MRI）由于其对软组织的先进成像能力，在脑癌的早期检测、诊断和分类方面非常有效。然而，人工检查脑部MRI扫描容易出错，并且严重依赖放射科医生的经验和疲劳程度。快速检测脑瘤对于确保患者安全至关重要。近年来，结合深度学习（DL）和机器学习（ML）技术的计算机辅助诊断（CAD）系统越来越受欢迎，因为它们使用先进的计算机视觉技术基于MRI图像提供精确的预测结果。本文介绍了一种名为ViT-PCA-RF的新型混合CAD方法，该方法将视觉Transformer（ViT）和主成分分析（PCA）与随机森林（RF）集成用于脑瘤分类，为该领域提供了一种新方法。使用ViT进行特征提取，PCA进行特征降维，RF进行脑瘤分类。所提出的ViT-PCA-RF模型有助于早期检测脑瘤，实现及时干预、改善患者预后并简化诊断过程，减少患者的时间和成本。我们的研究在脑瘤MRI（BTM）数据集上进行训练和测试，用于脑瘤的多分类。BTM数据集使用调整大小和归一化方法进行预处理，以确保输入一致。随后，我们的创新模型与传统分类器进行了比较，展示了令人印象深刻的性能指标。它分别以99%、99.4%、98.1%、98.1%和98.1%的准确率、特异性、精确率、召回率和F1分数表现出色。我们创新分类器的评估突出了我们模型的潜力，该模型利用了ViT、PCA和RF技术，在精确有效地检测脑瘤方面显示出前景。
