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

2025-12-06

AI Summary

To address the limited generalizability and poor validation performance of existing brain MRI tumor classification and segmentation models, this paper proposes a dual-architecture framework comprising SAETCN (Self-Attention Enhanced Classification Network) and SAS-Net (Self-Attention Guided Segmentation Network). SAETCN integrates self-attention mechanisms with deep convolutional layers to enhance robustness in multi-class tumor classification—namely glioma, meningioma, pituitary tumor, and non-tumor cases. SAS-Net employs self-attention–driven feature refinement to improve segmentation boundary localization. Both models are jointly trained and evaluated on a unified medical imaging dataset. The framework achieves 99.38% classification accuracy and 99.23% pixel-wise segmentation accuracy—outperforming state-of-the-art methods. This work delivers a high-accuracy, generalizable end-to-end solution for computer-aided diagnosis (CAD) of brain tumors.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Segmentation
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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%.

Problem

• Develops deep learning models for brain tumor classification from MRI scans
• Proposes novel architecture for accurate brain tumor segmentation in medical images
• Addresses limitations of existing models with high-accuracy automated diagnosis systems

Innovation

• Proposed SAETCN for brain tumor classification using self-attention
• Introduced SAS-Net for precise tumor segmentation with self-attention
• Achieved high accuracy on MRI images with novel deep learning architectures