Transfer Learning Driven Brain Tumor Detection via Deep CNN Architectures

In today’s medical imaging field, the classification of brain tumors plays a crucial role in determining the treatment plan, course of therapy, and survival rate. Our approach introduces a new technique that utilizes image-based deep learning models, specifically pre-trained neural networks, combined with a stacking algorithm for improved classification of brain tumors. Here, our method begins by processing T1-weighted images from MRI brain scans using multiple pre-trained CNNs. These neural networks extract visual features from the images, capturing intricate details crucial for accurate classification. To enhance accuracy, we employ an ensemble technique where the extracted image features serve as inputs to a single-layer stacking algorithm. This method integrates predictions from multiple base classifiers to make a final, more robust decision. Through its use of transfer learning, our approach leverages CNNs trained on extensive image datasets, ensuring that the extracted features are highly relevant for brain tumor classification. The combination of various base classifiers with a stacking algorithm further enhances classification accuracy. Our evaluation on two publicly available brain MRI image datasets demonstrates that this method significantly improves lesion detection, making it a promising step forward in medical imaging and healthcare.