Keywords: Diagnosis/Prediction, Diagnosis/Prediction
Motivation: Classification of rim-enhancing brain abscess, glioblastoma, and brain metastasis solely based on conventional MRI is challenging.
Goal(s): To develop a fully automatic system for classification of rim-enhancing lesions using deep learning with multi-modality MRI
Approach: An incremental strategy was used to train three models independently, including Model-B (CE-T1WI, T2WI, and T2-FLAIR), Model-BD (plus DWI and ADC), and Model-BDS (plus SWI). Furthermore, 3-T data were included to obtain Model-B+, BD+, and BDS+.
Results: Diffusion MRI improves the overall accuracy as expected, and the inclusion of 3T data possibly extend the flexibility of our model, reaching the highest accuracy of 0.796.
Impact: Our results demonstrated the value of multi-modality MRI in differentiation of three rim-enhancing lesions. We also highlighted the adaptability of our model on 1.5-T and 3-T data, possibly expanding its clinical use.
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