Enhancing Brain Tumor Classification Through Optimal Kernel Selection With GL1‐Regularization

ABSTRACT Brain tumors, known for their rapid and aggressive growth, are among the most serious and life‐threatening diseases worldwide. This makes the development of automated detection methods essential for saving lives. Deep transfer learning has become a highly effective approach for automating brain tumor classification and medical imaging, offering promising solutions on a global scale. However, leveraging a pretrained model typically involves special adaptations. Existing adaptation methods involve freezing or fine‐tuning specific layers without considering the contribution level of individual kernels. This work aims to extend the concept of layer‐level contributions to the kernel level by employing an adaptive optimization model. Indeed, this paper presents a novel optimization model that incorporates group lasso regularization to control which kernels are frozen and which are fine‐tuned. The proposed model selects optimal source features that contribute to the target task. Additionally, the proposed optimization model is solved utilizing proximal gradient descent. The method was evaluated on a three‐class brain tumor classification task, distinguishing between glioma, meningioma, and pituitary tumors, using a medical MRI dataset. Several experiments confirm the efficacy of our model in identifying both frozen and fine‐tuned kernels, thereby improving data classification. Subsequently, the results obtained are compared with those of state‐of‐the‐art transfer learning methods for comprehensive comparison.