Brain Tumor MRI Classification Dataset
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Brain tumors are among the most severe and life-threatening conditions affecting both children and adults. They constitute approximately 85-90% of all primary Central Nervous System (CNS) tumors, with an estimated 11,700 new cases diagnosed annually. The 5-year survival rate for individuals with malignant brain or CNS tumors is alarmingly low, at 34% for men and 36% for women. Brain tumors are categorized into various types, including benign, malignant, and pituitary tumors. Early and accurate diagnosis, coupled with effective treatment strategies, is critical to improving patient outcomes. Magnetic Resonance Imaging (MRI) is the most reliable method for detecting brain tumors, producing large volumes of image data for analysis. However, manual examination of these images can be prone to errors due to the inherent complexity of brain tumors.Automated classification methods leveraging Machine Learning (ML) and Artificial Intelligence (AI) have demonstrated significantly higher accuracy than manual analysis. Advanced deep learning techniques, such as Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Transfer Learning (TL), offer a promising solution for brain tumor detection and classification. These systems can support healthcare professionals in making more precise diagnoses, ultimately improving patient care worldwide.
脑肿瘤是危及儿童与成人生命健康的最严重疾病之一。其占所有原发性中枢神经系统 (Central Nervous System, CNS) 肿瘤的85%~90%，每年新增确诊病例约11700例。恶性脑肿瘤或中枢神经系统肿瘤患者的5年生存率低得令人担忧，男性为34%，女性为36%。脑肿瘤可分为多种类型，包括良性肿瘤、恶性肿瘤以及垂体腺瘤。尽早开展精准诊断，并配合有效的治疗方案，对改善患者预后至关重要。磁共振成像 (Magnetic Resonance Imaging, MRI) 是检测脑肿瘤最可靠的手段，可产生海量图像数据用于分析。但由于脑肿瘤本身的复杂性，人工阅片极易出现误差。依托机器学习 (Machine Learning, ML) 与人工智能 (Artificial Intelligence, AI) 的自动化分类方法，其准确率已显著高于人工分析。先进的深度学习技术，例如卷积神经网络 (Convolutional Neural Networks, CNN)、人工神经网络 (Artificial Neural Networks, ANN) 以及迁移学习 (Transfer Learning, TL)，为脑肿瘤的检测与分类提供了极具前景的解决方案。此类系统可辅助医疗从业者做出更精准的诊断，最终在全球范围内提升患者诊疗质量。
