🤖 AI Summary
Accurate classification of multiple brain tumor types—particularly meningiomas, which exhibit subtle imaging features—remains challenging in MRI. This study systematically evaluates the performance of five convolutional neural network architectures, including a custom model and established networks (VGG16, VGG19, DenseNet121, and EfficientNetB0), on approximately 10,000 clinical MRI images within a unified experimental framework. Employing consistent transfer learning and fine-tuning strategies, the findings indicate that architectural efficiency outweighs model depth in determining performance. EfficientNetB0 achieves a markedly superior overall accuracy of 95% and, notably, elevates the recall for meningiomas from approximately 20% to 89% on large-scale clinical data—a substantial improvement that underscores its significant clinical utility.
📝 Abstract
Despite recent advancements in deep learning, accurately classifying brain tumors from MRI images continues to pose challenges. In this research, we present a comprehensive evaluation of five different convolutional neural networks (CNN) architectures, including a customized baseline model and four pre-trained models - for use in classifying multi-class brain tumors using a clinically-sourced dataset of approximately 10,000 MRI images. We have utilized five different architectures; VGG16, VGG19, DenseNet121, and EfficientNetB0, which were all tested and trained within an identical experimental framework. Performance was measured by both overall accuracy and tumor-wise recall as a means to measure the clinically-relevant performance of each architecture. We found that EfficientNetB0 had the best overall classification accuracy at 95%, when compared to the other architectures tested; specifically VGG16 (94.37%), VGG19 (92.29%), DenseNet121 (90.91%) and the customized CNN (78.00%). An especially important finding of our research was the considerable improvement in detecting meningiomas; specifically, while simple CNNs could detect meningiomas with a recall rate of approximately 20%, EfficientNetB0 was able to detect meningiomas with a recall rate of 89%. Meningiomas are often difficult to detect because they can appear very subtly on MRI images. Additionally, an interesting finding was that the deeper VGG19 performed worse than the shallower VGG16. This indicates that in many cases the architectural efficiency of a CNN model may be more important than its depth when working with medical images. Overall, EfficientNetB0 appears to provide the optimal trade-off between classification accuracy, number of parameters used in the model and clinically meaningful performance.