Hybrid Deep Transfer Learning for Enhanced Brain Tumor Detection through the Integration of MobileNetV2 and InceptionV3

Brain Tumors (BT) are one of the most hazardous diseases in the world, primarily because of the speed at which they spread and high mortality rates. Hence, diagnosing BT at an early stage with higher accuracy is necessary. Recent breakthroughs in deep transfer learning have achieved great performances in identifying common types of BT, such as glioma, meningioma, and pituitary tumors. In this paper, we will propose a novel hybrid deep transfer learning architecture by combining the strengths of two state-of-the-art pre-trained deep convolutional neural networks: MobileNetV2 and InceptionV3. We implement a parallel feature extraction process, reduce dimensionality in the extracted features using dropout techniques, and pass the refined data through fully connected layers for accurate classification. In this context, our two-step methodology of sub-image classification and final image categorisation with the help of majority voting and maximum probability principles shows much-improved results. Validated on a comprehensive BT dataset, our approach achieves 98.68% accuracy, 97% precision, 97.25% recall, and 97% F-measure with 0.9975 AUC and average precision of 99.50%. The outcomes surpass the performance of existing models on precision, sensitivity, and generalization, thus revealing the value that this technique may hold in whole-slide MRI image classification and clinical practice.