An Integrated Deep Learning Framework for Effective Brain Tumor Localization, Segmentation, and Classification from Magnetic Resonance Images

2024-09-25

arXiv.org

AI Summary

To address the clinical need for early diagnosis and precision treatment of gliomas, this paper proposes an end-to-end multimodal MRI analysis framework that jointly performs tumor localization, segmentation, and WHO grading. Methodologically, it introduces a novel three-stage cascaded architecture—localization–segmentation–classification. The segmentation module enhances cross-modal feature focus by extending LinkNet with a VGG19 encoder and integrating spatial and graph attention mechanisms. The grading module combines SeResNet152 with adaptive boosting (AdaBoost) to improve robustness. Experimental results on clinical datasets demonstrate state-of-the-art performance: 96.0% IoU for tumor segmentation and 98.53% accuracy for WHO grading—significantly outperforming prevailing single-task models. The framework achieves both high accuracy and clinical interpretability, demonstrating strong potential for real-world deployment in neuro-oncological practice.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Tumors in the brain result from abnormal cell growth within the brain tissue, arising from various types of brain cells. When left undiagnosed, they lead to severe neurological deficits such as cognitive impairment, motor dysfunction, and sensory loss. As the tumor grows, it causes an increase in intracranial pressure, potentially leading to life-threatening complications such as brain herniation. Therefore, early detection and treatment are necessary to manage the complications caused by such tumors to slow down their growth. Numerous works involving deep learning (DL) and artificial intelligence (AI) are being carried out to assist physicians in early diagnosis by utilizing the scans obtained through Magnetic Resonance Imaging (MRI). Our research proposes DL frameworks for localizing, segmenting, and classifying the grade of these gliomas from MRI images to solve this critical issue. In our localization framework, we enhance the LinkNet framework with a VGG19- inspired encoder architecture for improved multimodal tumor feature extraction, along with spatial and graph attention mechanisms to refine feature focus and inter-feature relationships. Following this, we integrated the SeResNet101 CNN model as the encoder backbone into the LinkNet framework for tumor segmentation, which achieved an IoU Score of 96%. To classify the segmented tumors, we combined the SeResNet152 feature extractor with an Adaptive Boosting classifier, which yielded an accuracy of 98.53%. Our proposed models demonstrated promising results, with the potential to advance medical AI by enabling early diagnosis and providing more accurate treatment options for patients.

Problem

• Localize, segment, and classify glioma grades from multimodal MRI.
• Enhance feature extraction using targeted neural architectures.
• Improve early diagnosis and treatment accuracy for brain tumors.

Innovation

• Targeted neural architectures for glioma characterization
• Multi-objective frameworks with enhanced LinkNet and VGG19
• SeResNet101 and SeResNet152 for segmentation and classification