ArticleDiagnostics (Basel, Switzerland)2021
BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder-Decoder Network.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 99 citations in OpenAlex.
- SPiKe-Med: a hybrid spiking-CNN-transformer framework for energy-efficient brain tumor MRI segmentation.Frontiers in artificial intelligence · 2026Article
- mResU-Net: multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI.Medical & biological engineering & computing · 2024Review
- A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations.International journal of molecular sciences · 2023Article
- Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images.Diagnostics (Basel, Switzerland) · 2023Article
- Self-Enhanced Mixed Attention Network for Three-Modal Images Few-Shot Semantic Segmentation.Sensors (Basel, Switzerland) · 2023Article
- Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction.Diagnostics (Basel, Switzerland) · 2023Article
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- Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation.PloS one · 2023Article
- A survey on deep learning applied to medical images: from simple artificial neural networks to generative models.Neural computing & applications · 2023Review
- Smart Visualization of Medical Images as a Tool in the Function of Education in Neuroradiology.Diagnostics (Basel, Switzerland) · 2022Article
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- Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder.Journal of digital imaging · 2022Article
- AI-Based Pipeline for Classifying Pediatric Medulloblastoma Using Histopathological and Textural Images.Life (Basel, Switzerland) · 2022Article
- A Case Study of Quantizing Convolutional Neural Networks for Fast Disease Diagnosis on Portable Medical Devices.Sensors (Basel, Switzerland) · 2021Article
- Polish Multi-Institutional Study of Children with Ependymoma-Clinical Practice Outcomes in the Light of Prospective Trials.Diagnostics (Basel, Switzerland) · 2021Article
- State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods.Cancers · 2021Review
- Brain Cancer Prediction Based on Novel Interpretable Ensemble Gene Selection Algorithm and Classifier.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
Abstract
Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The brain tumor segmentation has proven to be a complicated task even for neural networks, due to the small-scale tumor regions. These small-scale tumor regions are unable to be identified, the reason being their tiny size and the huge difference between area occupancy by different tumor classes. In previous state-of-the-art neural network models, the biggest problem was that the location information along with spatial details gets lost in deeper layers. To address these problems, we have proposed an encoder-decoder based model named BrainSeg-Net. The Feature Enhancer (FE) block is incorporated into the BrainSeg-Net architecture which extracts the middle-level features from low-level features from the shallow layers and shares them with the dense layers. This feature aggregation helps to achieve better performance of tumor identification. To address the problem associated with imbalance class, we have used a custom-designed loss function. For evaluation of BrainSeg-Net architecture, three benchmark datasets are utilized: BraTS2017, BraTS 2018, and BraTS 2019. Segmentation of Enhancing Core (EC), Whole Tumor (WT), and Tumor Core (TC) is carried out. The proposed architecture have exhibited good improvement when compared with existing baseline and state-of-the-art techniques. The MR brain tumor segmentation by BrainSeg-Net uses enhanced location and spatial features, which performs better than the existing plethora of brain MR image segmentation approaches.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.