MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas

Motivation: Encephalitis and glioma can appear very similar in atypical cases. However, their treatment protocols differ significantly. As such, distinguishing between these two diseases is crucial. Goal(s): Our objective is to assess and compare the performance of various machine learning (ML) techniques in discriminating between encephalitis and glioma in atypical cases. Approach: We compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and assess the effectiveness of utilizing radiomics features extracted from both CML and DL in distinguishing encephalitis from glioma in atypical cases. Results: ML models can distinguish between encephalitis and glioma in atypical cases. Impact: Surgery is commonly considered as the initial treatment for glioma, while non-operative therapy is the primary approach for managing encephalitis. Precise identification of glioma and encephalitis facilitates physicians in avoiding misdiagnosis and delays in treatment.