Glioma Grade Classification Using CNNs and Segmentation With an Adaptive Approach Using Histogram Features in Brain MRIs

Abstract

Artificial intelligence (AI) applications have become popular due to their advantages in solving health problems with high accuracy and confidence. One such application is the diagnosis of brain tumors or anomalies. This paper presents two new approaches for brain tumor grade classification and segmentation. Convolutional neural network (CNN) models were used as the first approach to classify High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) tumors and achieved with 99.85% accuracy, 99.85% F1 and 99.92% AUC scores. A new pipeline consisting of normalization, modality fusion and CNN model for HGG-LGG classification tasks was also proposed and developed. A novel algorithm based on histograms, thresholding and morphological filtering with feature fusion was also proposed and developed for the segmentation task. 70.58% Dice Similarity (DS) on average was achieved with the complete tumor segmentation. Experimental results have shown that the proposed algorithm has improved to measure the complete tumor region 15% more compared to the fixed thresholding. Segmentation results also encourage that the algorithm can be used as a feature extraction process on different sized brain MR images. It is expected that the extracted center of gravity features can be further used in AI algorithms for better segmentation, including T1 and T1CE modalities.