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Brain tumours within the skull can lead to serious health issues. The rapid and accurate detection and segmentation of tumour regions allow patients to receive appropriate treatment at an early stage, increasing their chances of recovery and survival. Various medical imaging methods, such as Magnetic Resonance Imaging (MRI), Positron and digital pathology, Emission Tomography (PET), Computed Tomography (CT) are used for the detection of brain tumours. Nowadays, with advancing technology and hardware, concepts like artificial intelligence and deep learning (DL) are becoming increasingly popular. Many artificial intelligence methods are also being utilized in studies on brain tumour segmentation. This paper proposes a 3D U-Net DL model for brain tumour segmentation. The training and testing processes are carried out on the Brain Tumour Segmentation (BraTS) 2020 dataset, which is widely used in the literature. As a result, an Intersection over Union (IoU) score of 0.81, a dice score of 0.87 and a pixel accuracy of 0.99 are achieved. The proposed model has the potential to assist experts in diagnosing the disease and developing appropriate treatment plans, thanks to its ability to segment brain tumours quickly and with high accuracy.
Ateş et al. (Wed,) studied this question.