The early detection and localization of brain tumours in magnetic resonance imaging (MRI) data play a pivotal role in diagnosis and therapy planning. Timely detection and accurate localization are crucial for effective diagnosis and therapy planning. The study aims to improve early detection and localization of brain tumours in MRI data by introducing a unified assessment system that integrates several image processing components, including localization, detection and classification. It also leverages the Ridgelet transforms unique capabilities for detection of precise the location of tumour. In contrast to other approaches, the study uses the Ridgelet transform as its sole approach for localizing tumours. This research incorporates the categorization of MRI into tumorous and non-tumorous and further into Glioma and meningioma by employing Ridgelet transform in conjunction with Grey Level Co-occurrence matrix for feature extraction and classification using Multi-Layer Preceptron (MLP). This further ensures thorough evaluation of the localization process. The Ridgelet-based methodology achieved an accuracy of 97.32% for classifying MRI into tumorous and non-tumorous classifications. Additionally, the study explores the use of the Radon transform in conjunction with GLCM for tumour classification, yielding results with an overall accuracy of 97.32%. The paper offers an innovative and efficient methodology and highlights the significant importance of the Ridgelet transform in tumour localization. The results are more robust when compared to known models, which highlights the potential contribution of Ridgelet transform to the advancement of brain tumour characterization in MRI data.

磁共振成像（MRI）数据中脑肿瘤的早期检测和定位在诊断和治疗规划中发挥着关键作用。及时检测和准确定位对于有效的诊断和治疗计划至关重要。该研究旨在通过引入集成了多个图像处理组件（包括定位、检测和分类）的统一评估系统，改善 MRI 数据中脑肿瘤的早期检测和定位。它还利用 Ridgelet 变换的独特功能来检测肿瘤的精确位置。与其他方法相比，该研究使用 Ridgelet 变换作为定位肿瘤的唯一方法。本研究通过采用 Ridgelet 变换结合灰度共生矩阵，使用多层预感知器 (MLP) 进行特征提取和分类，将 MRI 分类为肿瘤和非肿瘤，并进一步分类为神经胶质瘤和脑膜瘤。这进一步确保了本地化过程的彻底评估。基于 Ridgelet 的方法将 MRI 分为肿瘤和非肿瘤分类的准确率达到 97.32%。此外，该研究探索了将 Radon 变换与 GLCM 结合使用进行肿瘤分类，得到的结果总体准确率为 97.32%。该论文提供了一种创新且有效的方法，并强调了 Ridgelet 变换在肿瘤定位中的重要性。与已知模型相比，结果更加稳健，这凸显了 Ridgelet 变换对 MRI 数据中脑肿瘤表征进展的潜在贡献。