Transfer Learning-Driven MRI-Based Classification Pipelines for Brain Tumor Diagnosis: Glioma, Meningioma, and Pituitary Tumor Discrimination

Abstract

MRI-based classification of brain tumors is an important step in delivering timely and effective treatment. In this study, we tested a method that uses CNNs trained via transfer learning to classify the three main types of brain tumors (gliomas, meningiomas, and pituitary tumors). Mendeley dataset is taken into consideration, containing 6,056 MRI images (2004 brain glioma, 2004 brain meningioma, 2048 brain pituitary). Two types of CNN architectures were tested, AlexNet (trained from scratch) and InceptionV3 (using the weights from ImageNet). All of the images were preprocessed before being fed to the models using image resizing, normalization, and extensive augmentation to ensure accuracy and minimize class imbalance between the tumor categories. Effectively stratified train-test splits of the data allowed for fair performance evaluation of both models. The AlexNet model consistently achieved 94% accuracy, with a precision, recall, and F1-score of 94%, indicating that it could provide reliable performance when classifying brain tumors based on MRI. In contrast, the InceptionV3 model using transfer learning and fine-tuning performed even better than AlexNet, achieving 98% accuracy with a precision, recall, and F1- score of 98%. These results indicate that pre-trained convolutional neural network architectures provide increased classification reliability, significantly reduce training time, and are applicable to medical datasets that contain limited numbers of instances. The findings of this research study illustrate the potential for developing highly accurate and efficient automated deep learning technology to accurately diagnose neuro-oncology diseases using transfer learning. This type of technology will provide a strong basis for Clinical Decision Support Systems (CDSS) that aid radiologists with the interpretation of diagnostic medical images.