Title: Segnet and U-Net based brain tumour segmentation

Authors: R. Ashwini; Swagata Sarkar; C. Pandi; S. Rajalakshmi

Addresses: Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai – 600077, India ' Artificial Intelligence and Data Science Department, Sri Sairam Engineering College, West Tambaram, Chennai, India ' Department of Computer Science and Engineering, Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai, India ' Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Pennalur, Sriperumbudur – 602117, India

Abstract: The process of separating individual brain tumours in diagnostic pictures is an essential component of therapeutic therapy. The manual segmentation process takes a lot of time and requires a lot of work, while the current automated segmentation techniques have problems such as a large number of parameters and a lack of accuracy. We created a completely automated technique for the segmentation of brain tumours by applying deep learning. The system was tested on 285 examples of brain tumours using multi-parametric magnet resonant images taken from either the BraTS2018 data set. The quantitative study of brain tumours is helpful in gaining a better knowledge of the features of the tumour as well as in developing more effective treatment strategies. Through the use of this technology, it was possible to get mean dice values of 0.9213 for the overall tumour and 0.8729 for the tumour core.

Keywords: brain tumour; BraTS database; computer intelligence; Densenet; U-Net.

DOI: 10.1504/IJMEI.2025.148642

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.5, pp.453 - 462

Received: 28 Sep 2022
Accepted: 19 Dec 2022
Published online: 17 Sep 2025 *

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