End-to-end brain tumor detection using a graph-feature-based classifier

Mingzhe Hu; Jing Wang; Chih-Wei Chang; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2654020

10 April 2023 End-to-end brain tumor detection using a graph-feature-based classifier

Mingzhe Hu, Jing Wang, Chih-Wei Chang, Tian Liu, Xiaofeng Yang

Proceedings Volume 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging; 124681C (2023) https://doi.org/10.1117/12.2654020
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Brain tumors are caused by abnormal cell growth and can cause pain and reduced survival rates. The early detection of brain tumors is pivotal in improving outcomes. Recently, magnetic resonance imaging (MRI) has been widely deployed in clinics to diagnose brain lesions non-invasively and prevent patients from receiving radiation doses of diagnostic imaging modalities. Traditionally, medical oncologists and radiologists diagnose brain tumors as benign or malignant using visual analysis of MRI images. The decision-making process is labor intensive, and relies on the expertise level of physicians. Recently, deep learning has dramatically changed the landscape of oncology by enabling automatic and accurate diagnosis. While the backbones of most state-of-the-art architectures are convolutional neural networks or vision transformers, the application of graph neural networks in radiation oncology has not yet been explored. To the authors' knowledge, this is the first demonstration of using fully-automated graph-feature-based classifiers for end-to-end brain tumor detection, indicating an overall classification accuracy of 94.89%. The proposed graph-feature-based classifiers are accessible for clinical implementation and could potentially assist radiation oncologists to precisely and accurately diagnose and prognosticate brain lesions.

Citation Download Citation

Mingzhe Hu, Jing Wang, Chih-Wei Chang, Tian Liu, and Xiaofeng Yang "End-to-end brain tumor detection using a graph-feature-based classifier", Proc. SPIE 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging, 124681C (10 April 2023); https://doi.org/10.1117/12.2654020

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