[2511.18326] General vs Domain-Specific CNNs: Understanding Pretraining Effects on Brain MRI Tumor Classification

Computer Science > Computer Vision and Pattern Recognition arXiv:2511.18326 (cs) [Submitted on 23 Nov 2025 (v1), last revised 27 Feb 2026 (this version, v2)] Title:General vs Domain-Specific CNNs: Understanding Pretraining Effects on Brain MRI Tumor Classification Authors:Helia Abedini, Saba Rahimi, Reza Vaziri View a PDF of the paper titled General vs Domain-Specific CNNs: Understanding Pretraining Effects on Brain MRI Tumor Classification, by Helia Abedini and 2 other authors View PDF Abstract:The accurate identification of brain tumors from magnetic resonance imaging (MRI) is essential for timely diagnosis and effective therapeutic intervention. While deep convolutional neural networks (CNNs), particularly those pre-trained on extensive datasets, have shown considerable promise in medical image analysis, a key question arises when working with limited data: do models pre-trained on specialized medical image repositories outperform those pre-trained on diverse, general-domain datasets? This research presents a comparative analysis of three distinct pre-trained CNN architectures for brain tumor classification: RadImageNet DenseNet121, which leverages pre-training on medical-domain data, alongside two modern general-purpose networks, EfficientNetV2S and ConvNeXt-Tiny. All models were trained and fine-tuned under uniform experimental conditions using a modestly sized brain MRI dataset to maintain consistency in evaluation. The experimental outcomes indicate that ConvNeXt-Ti...