新型深度学习架构在脑部MRI图像肿瘤分类与分割中的应用

摘要

脑肿瘤对人类生命构成重大威胁，因此在早期阶段精准检测对改善诊疗效果至关重要。目前放射科医生主要通过核磁共振成像扫描图像进行人工诊断，但近年来儿童与青少年脑肿瘤发病率上升导致数据量激增，使得人工检测耗时且困难。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）用于脑肿瘤分类的自注意力增强肿瘤分类网络，在包含胶质瘤、脑膜瘤、垂体瘤及非肿瘤病例的数据集上训练后，验证集准确率达99.38%，成为少数能实现精准检测的创新深度学习架构；（b）用于精确分割脑肿瘤的自注意力分割网络，其整体像素精度达到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%.

新型深度学习架构在脑部MRI图像肿瘤分类与分割中的应用

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

摘要

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