Usefulness of Deep Learning Techniques Using Magnetic Resonance Imaging for the Diagnosis of Meningioma and Atypical Meningioma

This study aimed to implement an artificial intelligence (AI) model capable of diagnosing meningioma and atypical meningioma during deep learning using magnetic resonance imaging (MRI). The experimental method was to acquire MRI scans of meningiomas and atypical meningiomas using the T2 weighted imaging (T2WI), T1 weighted imaging (T1WI), contrast enhanced T1WI (CE-T1WI), and contrast enhanced fluid attenuated inversion recovery (CE-FLAIR) methods. The MRI results, according to each method, were categorized into two classes for diagnosing either meningioma or atypical meningioma. The CE-FLAIR images tended to have lower learning performance compared to other methods, but all methods showed excellent diagnostic performance. We confirmed that deep learning is a useful method for diagnosing meningioma and atypical meningioma. When using MRI, if the accuracy and loss rate are improved by applying deep learning optimized for medical images, it will be possible to implement a brain tumor diagnosis model with better learning performance.