IRAM–NET model: image residual agnostics meta-learning-based network for rare de novo glioblastoma diagnosis

Abstract

In the recent years, neuroimaging and deep learning have received notable scientific attention for the diagnosis of grade IV tumor de novo glioblastoma in the central nervous system. However, the scarce amount of neuroimaging data for training has resulted in significant overfitting issues for numerous deep learning models. To address these challenges, we propose the implementation of a meta-learning-based IRAM–NET model that utilizes the ResNet-50 as a deep learning-based model and incorporates the e-MAML ensemble technique from meta-learning for the early diagnosis of glioblastoma. The methodology developed was trained and validated using brain MRI images taken from numerous national and international cancer initiative data repositories. In the training phase, this study employed detailed procedures, including the handling of exceptions and the application of normalization techniques. These measures were implemented to guarantee precise data representation, mitigate the risk of overfitting, and enhance the proposed model’s capacity for making meaningful generalizations. The proposed IRAM–NET model surpasses the most recent studies in accurately predicting glioblastoma diagnosis, achieving a training, testing and validation accuracy of 97.22%, 96.10%, and 94.74%, respectively. Overall, the research not only enhances the diagnosis of rare disorders like glioblastoma, but also promotes the wider inclusion of meta-learning in healthcare. This underlines the importance of adaptation and efficiency in situations with limited data availability.