A Novel Mixed Convolution Transformer Model for the Fast and Accurate Diagnosis of Glioma Subtypes

Abstract

Glioblastoma is the most common adult brain tumor, significantly impacts disability and mortality. Early and accurate diagnosis of glioma subtypes is essential, but manual categorization is challenging due to their complexity, prompting the need for automated solutions. We developed an innovative mixed convolution-transformer model to classify glioma subtypes, including astrocytoma, glioblastoma, oligodendroglioma, and normal brain tissue, using whole slide images. The novelty of this model lies in its remarkable efficiency and precise results. Multiple advanced and complex layers are incorporated during its development to enhance its performance, ensuring that it delivers fast and accurate classification results for glioma. This proposed model obtains an overall training accuracy of 97.41%, peaking at 98.12% for validation and 97.35% for testing. Next, our model architecture is independently evaluated by comparing its training performances on the CIFAR-10 and CIFAR-100 datasets with the vision transformer and compact convolutional transformer models. Results across various datasets demonstrate that the model consistently outperforms existing models. This performance underscores the effectiveness of our proposed approach in classifying glioma subtypes accurately and efficiently, highlighting its potential impact on healthcare and disability. This system enhances the classification of glioma subtypes and facilitates swift identification, ensuring appropriate and timely treatment.