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Segmentation and classification of brain tumor using 3D-UNet deep neural networks

2022; Elsevier BV; Volume: 3; Linguagem: Inglês

10.1016/j.ijcce.2022.11.001

2666-3074

Pranjal Agrawal, Nitish Katal, Nishtha Hooda,

Digital Imaging for Blood Diseases

Early detection and diagnosis of a brain tumour enhance the medical options and the patient's chance of recovery. Magnetic resonance imaging (MRI) is used to detect and diagnose brain tumours. However, the manual identification of brain tumours from a large number of MRI images in clinical practice solely depends on the time and experience of medical professionals. Presently, computer aided expert systems are booming to facilitate medical diagnosis and treatment recommendations. Numerous machine learning and deep learning based frameworks are employed for brain tumour detection. This paper aims to design an efficient framework for brain tumour segmentation and classification using deep learning techniques. The study employs the 3D-UNet model for the volumetric segmentation of the MRI images, followed by the classification of the tumour using CNNs. The loss and precision diagrams are presented to establish the validity of the models. The performance of proposed models is measured, and the results are compared with those of other approaches reported in the literature. It is found that the proposed work is more efficacious than the state-of-the-art techniques.