Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images

Abstract

The precise identification of FLAIR abnormalities in brain MR images is essential for diagnosing and managing lower-grade gliomas, segmentation continues to be a difficult task. In this research, we develop an exhaustive strategy that integrates advanced deep learning models such as DeepLabv3, U-Net, DenseNet121-Unet, ResNet50, Attention U-Net and EfficientNet to effectively segment FLAIR abnormalities in a dataset comprising 110 lower-grade glioma patients. The cancer Imaging achieve (TCIA), includes genomic cluster data and patient-specific details. Our methodology tackles the multi-class data imbalanced by employing a customized loss function, which merges Categorical Cross Entropy (CCE) WCE and WMDL functions are used to calculate loss, allowing the network to accurately segment smaller tumor regions. By performing dense network training on 3D picture patches, the suggested technique improves detection of border region artifacts and efficiently manages storage and system limited resources. We evaluate our strategy’s effectiveness on the presented dataset, emphasizing its potential for assisting correct diagnosis and individualized treatment strategies for patients with lower-grade gliomas.