Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks

• Published in: Scientific reports Vol. 14; no. 1; p. 9501
• Main Authors: Abd-Ellah, Mahmoud Khaled, Awad, Ali Ismail, Khalaf, Ashraf A. M., Ibraheem, Amira Mofreh
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25-04-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/985; 639/705/117; 639/705/794; 639/925/930/1032; 692/700/1421/1628; Brain - diagnostic imaging; Brain - pathology; Brain cancer; Brain Neoplasms - diagnosis; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain tumors; Classification; Deep Learning; Diagnosis; Glioblastoma; Glioblastoma - diagnosis; Glioblastoma - diagnostic imaging; Glioblastoma - pathology; Glioma; Glioma - diagnosis; Glioma - diagnostic imaging; Glioma - pathology; Humanities and Social Sciences; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; multidisciplinary; Neural networks; Neural Networks, Computer; Neuroimaging; Science; Science (multidisciplinary); Segmentation; Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-59566-7

The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature.