General vs Domain-Specific CNNs: Understanding Pretraining Effects on Brain MRI Tumor Classification

Research comparing CNN architectures for brain tumor classification found that general-purpose models like ConvNeXt-Tiny (93% accuracy) outperformed domain-specific medical pre-trained models like RadImageNet DenseNet121 (68% accuracy). The study suggests that contemporary general-purpose CNNs with diverse pre-training may be more effective for medical imaging tasks in data-scarce scenarios.

• →ConvNeXt-Tiny achieved 93% accuracy in brain tumor classification, significantly outperforming medical domain-specific models.
• →RadImageNet DenseNet121, despite medical-domain pre-training, only achieved 68% accuracy with limited generalization.
• →General-purpose CNNs pre-trained on diverse datasets may offer superior transfer learning for specialized medical tasks.
• →Domain-specific pre-training does not guarantee better performance in data-scarce medical imaging scenarios.
• →Modern CNN architectures with deeper networks show promise for medical diagnostic applications.