Multi-class brain malignant tumor diagnosis in magnetic resonance imaging using convolutional neural networks.
Authors
Affiliations (7)
Affiliations (7)
- Department of Neurosurgery, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Qingchun Road, No. 3, Hangzhou, Zhejiang 310016, China. Electronic address: [email protected].
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Jinji Road No.1, Guilin, Guangxi 541004, China. Electronic address: [email protected].
- Zhejiang University - Universityof Illinois Urbana-Champaign Institute,Zhejiang University, Haizhou East Road No. 718, Haining, Zhejiang 314400, China. Electronic address: [email protected].
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, Hong Kong SAR. Electronic address: [email protected].
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Qingchun Road, No. 3, Hangzhou, Zhejiang 310016, China. Electronic address: [email protected].
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Jinji Road No.1, Guilin, Guangxi 541004, China. Electronic address: [email protected].
- Zhejiang University - Universityof Illinois Urbana-Champaign Institute,Zhejiang University, Haizhou East Road No. 718, Haining, Zhejiang 314400, China. Electronic address: [email protected].
Abstract
Glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) are common malignant brain tumors with similar radiological features, while the accurate and non-invasive dialgnosis is essential for selecting appropriate treatment plans. This study develops a deep learning model, FoTNet, to improve the automatic diagnosis accuracy of these tumors, particularly for the relatively rare PCNSL tumor. The model integrates a frequency-based channel attention layer and the focal loss to address the class imbalance issue caused by the limited samples of PCNSL. A multi-center MRI dataset was constructed by collecting and integrating data from Sir Run Run Shaw Hospital, along with public datasets from UPENN and TCGA. The dataset includes T1-weighted contrast-enhanced (T1-CE) MRI images from 58 GBM, 82 PCNSL, and 269 BM cases, which were divided into training and testing sets with a 5:2 ratio. FoTNet achieved a classification accuracy of 92.5 % and an average AUC of 0.9754 on the test set, significantly outperforming existing machine learning and deep learning methods in distinguishing among GBM, PCNSL, and BM. Through multiple validations, FoTNet has proven to be an effective and robust tool for accurately classifying these brain tumors, providing strong support for preoperative diagnosis and assisting clinicians in making more informed treatment decisions.