Astrocytomas are among the most prevalent primary brain tumors and are classified into four grades by the World Health Organization. Accurate grading is essential for guiding treatment, as therapeutic strategies depend heavily on tumor grade. This paper presents a new preoperative classification method for astrocytomas, addressing the issue of data scarcity in medical imaging. This work leverages an advanced statistical modeling approach based on stochastic differential equations to analyze post-contrast T1-weighted brain MRI images that require minimal data and offer rapid processing times. In this method, the alpha-stable nature of MRI images is represented by applying a fractional Laplacian filter, and the parameters of the resulting alpha-stable distribution are fed to classifiers to detect the grade of astrocytomas. The method is implemented in both 1D and 2D processing modes, with customized preprocessing for each. Three classification algorithms were evaluated: support vector machine, K-nearest neighbor, and random forest. In the three-class classification task (Grades II-IV), the support vector machine exhibited superior performance, achieving accuracy, sensitivity, and specificity of 98.49%, 98.42%, and 99.23% in 2D mode, and 93.52%, 93.23%, and 96.72% in 1D mode. The results indicate that the proposed framework has the potential to significantly enhance preoperative grading of astrocytomas.