ArticleScientific reports2024
Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- W-AGRU-Net: a dual-stream attention framework for robust brain tumor segmentation in clinical MRI imaging.Scientific reports · 2026Article
- Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic and Critical Review on Methodological Approaches to Glioma Segmentation and Classification Through Multiparametric MRI.Brain sciences · 2026Review
- Adaptive multi-feature fusion architecture with optimized learning for high-fidelity brain tumor classification in MRI.Scientific reports · 2026Article
- Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis.International journal of biomedical imaging · 2026Article
- An effective flowchart for multimodal brain tumor binary classification with ranked 3D texture features.Scientific reports · 2025Article
- T1-weighted MRI-based brain tumor classification using hybrid deep learning models.Scientific reports · 2025Article
- LIU-NET: lightweight Inception U-Net for efficient brain tumor segmentation from multimodal 3D MRI images.PeerJ. Computer science · 2025Article
- XAI-MRI: an ensemble dual-modality approach for 3D brain tumor segmentation using magnetic resonance imaging.Frontiers in artificial intelligence · 2025Article
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Authors and funding
4 authors.
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Abstract
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.
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