developing an artificial intelligence model for tumor grading and classification, based on mri sequences of human brain gliomas

Background: artificial intelligence (ai) models provide advanced applications to many scientific areas, including the prediction of the pathologic grade of tumors, utilizing radiology techniques. gliomas are among the malignant brain tumors in human adults, and their efficient diagnosis is of high clinical significance. objectives: given the contribution of ai tomedical diagnoses, we investigated the role of deep learning in the differential diagnosis and grading of human brain gliomas. methods: this study developed a new ai diagnostic model, i.e., efficientnetb0, to grade and classify human brain gliomas, using sequences from magnetic resonance imaging (mri). results: we validated the new aimodel, using a standard dataset (brats-2019) and demonstrated that the ai components, i.e., convolutional neural networks and transfer learning, provided excellent performance for classifying and grading glioma images at 98.8% accuracy. conclusions: the proposed model, efficientnetb0, is capable of classifying and grading glioma from mri sequences at high accuracy, validity, and specificity. it can provide better performance and diagnostic results for human glioma images than models developed by previous studies.