Analysis of Deep Learning Algorithms Using ConvNeXt and Vision Transformer for Brain Tumor Disease - Ekayanda, Gilang; Rahardi, Majid (Politeknik Negeri Batam, 2025)
This study aims to conduct a comparative analysis and identify the most effective deep learning architecture between ConvNeXt and Vision Transformer (ViT) for the automated classification of brain tumors from MRI imagery. Rapid and accurate brain tumor diagnosis is crucial; however, the manual interpretation of MRI scans is time-consuming and reliant on specialist expertise, creating an urgent need for reliable automation in brain tumor diagnosis. This research utilizes a dataset of 4,600 images, balanced between 2,513 'Brain Tumor' and 2,087 'Healthy' instances. A robust 5-Fold Cross-Validation methodology was employed to evaluate model performance, wherein the data was divided into five folds, each consisting of 920 images, ensuring every image served as both training and testing data. The quantitative results demonstrated high efficacy from both models, although ConvNeXt achieved a slight, consistent advantage. ConvNeXt obtained an accuracy of 99.13%, precision of 99.13%, recall of 99.13%, and an F1-Score of 99.13%. In comparison, the ViT model scored an accuracy of 98.13%, precision of 98.14%, recall of 98.13%, and an F1-Score of 98.13%. This quantitative superiority was validated through qualitative analysis using saliency maps, which confirmed that the models' computational attention was accurately focused on the anatomical locations of the actual tumor lesions.