Enhancing Brain Tumor Diagnosis with Optimized Deep Convolutional Transfer Learning Models

Abstract

Brain tumors represent a critical and aggressive class of neurological disorders, often leading to significantly reduced life expectancy, particularly in their advanced stages. This study introduces an enhanced diagnostic approach leveraging optimized deep convolutional neural networks (CNNs) integrated with transfer learning to improve the accuracy and efficiency of brain tumor classification. Specifically, we employ a fine-tuned MobileNetV2 model to categorize brain MRI images into four classes: gliomas, meningiomas, pituitary tumors, and non-tumorous conditions. The proposed method achieves a high classification accuracy of 98.33%, with a precision of 0.98, recall of 0.97, and specificity of 0.99, under an 80:20 train-test split. Notably, our approach maintains low computational complexity while delivering superior performance compared to existing models. These results highlight the potential of our optimized transfer learning framework as a reliable and time-efficient tool for aiding clinical decision-making in brain tumor diagnosis.