Abstract:

Intracranial tumors represent a serious neurological disorder, and early detection is critical for improving patient survival rates. However, current deep learning models for intracranial tumor image classification often suffer from insufficient feature extraction, high model complexity, and class imbalance. To address these challenges, this study proposes a lightweight deep learning architecture, the adaptive dynamic network (AD-Net). The network innovatively incorporates a dynamic convolution mechanism that adaptively adjusts filter responses, thereby enhancing the representation of complex and imbalanced tumor features. Additionally, the integration of a channel attention mechanism enables the model to focus on critical channel information, further improving classification accuracy and interpretability. This study also introduces a combined binary and ternary classification training strategy, which significantly reduces training time and computational resource requirements, making the model more suitable for resource-constrained medical settings. Experimental results demonstrate that AD-Net outperforms existing mainstream deep learning models in accuracy, precision, recall, F1 score, and Cohen’s Kappa coefficient, confirming its effectiveness and practical value for intracranial tumor classification.

Key words: intracranial tumor classification, convolutional neural network (CNN), dynamic convolution, channel attention mechanism, lightweight model