基于新型深度学习架构的脑部MRI图像肿瘤分类与分割方法

Novel Deep Learning Architectures for Classification and Segmentation of Brain Tumors from MRI Images

December 6, 2025

作者: Sayan Das, Arghadip Biswas

cs.AI

摘要

脑肿瘤对人类生命构成重大威胁，因此在早期阶段实现精准检测对改善诊疗效果至关重要。目前放射科医生通常通过患者MRI扫描图像进行人工识别，但近年来儿童和青少年脑肿瘤发病率上升导致数据量激增，使得人工检测既耗时又困难。随着人工智能在现代社会的兴起及其在医疗领域的广泛应用，我们可借助计算机辅助诊断系统实现脑肿瘤的自动早期检测。现有模型普遍存在泛化能力不足、验证集表现欠佳的问题。为此，我们提出两种新型深度学习架构：(a) SAETCN（自注意力增强肿瘤分类网络）用于实现不同类型脑肿瘤的分类，在验证集上达到99.38%的准确率，使其成为少数能精准检测脑肿瘤的新型深度学习架构之一。该模型基于包含三类肿瘤（胶质瘤、脑膜瘤、垂体瘤）及非肿瘤病例的数据集进行训练；(b) SAS-Net（自注意力分割网络）用于实现脑肿瘤的精确分割，整体像素精度达到99.23%。

English

Brain tumors pose a significant threat to human life, therefore it is very much necessary to detect them accurately in the early stages for better diagnosis and treatment. Brain tumors can be detected by the radiologist manually from the MRI scan images of the patients. However, the incidence of brain tumors has risen amongst children and adolescents in recent years, resulting in a substantial volume of data, as a result, it is time-consuming and difficult to detect manually. With the emergence of Artificial intelligence in the modern world and its vast application in the medical field, we can make an approach to the CAD (Computer Aided Diagnosis) system for the early detection of Brain tumors automatically. All the existing models for this task are not completely generalized and perform poorly on the validation data. So, we have proposed two novel Deep Learning Architectures - (a) SAETCN (Self-Attention Enhancement Tumor Classification Network) for the classification of different kinds of brain tumors. We have achieved an accuracy of 99.38% on the validation dataset making it one of the few Novel Deep learning-based architecture that is capable of detecting brain tumors accurately. We have trained the model on the dataset, which contains images of 3 types of tumors (glioma, meningioma, and pituitary tumors) and non-tumor cases. and (b) SAS-Net (Self-Attentive Segmentation Network) for the accurate segmentation of brain tumors. We have achieved an overall pixel accuracy of 99.23%.