Customized CNN Architectures Outperform Pre-Trained Models in Differentiating Normal Brain Tissues, Glioma,...

Early and accurate detection of brain tumors is essential for improving treatment outcomes and patient survival. While pre-trained deep learning models such as ResNet, VGG, and MobileNet have achieved notable success in medical image classification, their generalized architectures often struggle to capture the intricate heterogeneity of brain tissues. This study introduces a customized Convolutional Neural Network (CNN) specifically designed for brain tumor classification, demonstrating superior performance over widely used pre-trained models. The primary objective of this research is to evaluate the diagnostic performance of the customized CNN and pre-trained models in real-world scenarios and establish benchmarks for their accuracy, reliability, and computational efficiency. Furthermore, this study aims to explore and implement optimization techniques that enhance the diagnostic capabilities of the models under investigation. The proposed CNN incorporates optimized convolutional blocks, adaptive fine-tuning, and an advanced data augmentation pipeline to enhance feature extraction and minimize overfitting. When evaluated on the CE-MRI Figshare dataset containing 3064 T1-weighted contrast-enhanced images, the model achieved a validation accuracy of 97.01%, outperforming ResNet50 (89.15%), MobileNetV2 (92.89%), and VGG16 (96.76%). Furthermore, the CNN exhibited strong consistency across all tumor categories-glioma, meningioma, and pituitary tumors-proving its robustness in real-world diagnostic scenarios. These findings confirm that a well-optimized CNN architecture can outperform generic pre-trained models, underscoring the importance of task-specific deep learning designs for medical imaging applications.