Automated meningioma detection using skull X ray images with deep learning and machine learning classifiers.

ABSTRACT: This study aimed to develop a novel diagnostic tool for detecting meningioma using skull X-ray images, combining deep learning with traditional machine learning classifiers. The goal was to explore the potential of using a cost-effective and widely accessible imaging modality for automated meningioma detection. Skull X-ray images from 158 meningioma patients (632 images) and 201 control subjects (804 images) without brain tumors or vascular diseases were retrospectively collected from St. Vincent's Hospital, South Korea. Anteroposterior, Towne, and lateral views were included in the analysis. EfficientNetB0 served as a backbone of the deep learning model. It was enhanced with transfer learning and attention mechanisms. Extracted features were integrated into traditional classifiers such as

SUBMITTER: Kim HU 

PROVIDER: S-EPMC12624126 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature