IEEE ACCESS, cilt.1, sa.1, ss.232-242, 2023 (SCI-Expanded)
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 might be further used in AI algorithms for better
segmentation including T1 and T1CE modalities.