Feature selection and classification of brain midline tumors using tiny vision transformers and neighborhood component analysis

Brain midline tumors pose significant diagnostic challenges due to their complex structure and variability in presentation. In this study, we propose a robust classification framework for detecting brain midline tumors using image classification and feature selection techniques. The images, categorized into positive and negative classes, were processed using the Google Tiny Model Vision Transformer without any additional training. Feature extraction was performed by utilizing the head layer of the Tiny Model Vision Transformer, which yielded 1,000 features from the fully connected layer based on the pre-trained weights of the network. These features were initially classified using classical machine learning classifiers such as support vector machines, k-nearest neighbors and decision trees. To improve classification accuracy and reduce computational costs, Neighborhood Component Analysis was applied as a feature selection method. Neighborhood Component Analysis selected the top 460 most informative features from the initial set of 1,000 features. These selected features were subsequently used for classification, and the performance was compared with the results obtained using the full feature set. The comparative analysis revealed that the Neighborhood Component Analysis-based feature selection significantly enhanced classification accuracy and reduced processing time without sacrificing model reliability. The findings demonstrate that combining Tiny Model Vision Transformers with Neighborhood Component Analysis is an effective approach for brain midline tumor classification, offering a balance between accuracy and computational efficiency. This method holds promise for improving early diagnosis and aiding clinical decision-making, making it a valuable tool in medical image analysis and brain tumor detection.