ArticleDiagnostics (Basel, Switzerland)2023
Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Predicting the progression risk of chronic obstructive pulmonary disease in high-risk individuals based on whole-lung radiomics and deep learning: a multicenter study.Journal of thoracic disease · 2026Article
- [Research progress of multimodal magnetic resonance imaging brain tumor segmentation based on fused neural network model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- CMRA-DETR: a lightweight and high-accuracy detection framework for MRI-based brain tumor identification.Frontiers in medicine · 2026Article
- SPiKe-Med: a hybrid spiking-CNN-transformer framework for energy-efficient brain tumor MRI segmentation.Frontiers in artificial intelligence · 2026Article
- Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.Scientific reports · 2025Article
- Exploring neonatal brain tumors: a narrative review about epidemiology, classification, and management.Annals of medicine and surgery (2012) · 2025Review
- A brain tumor segmentation method based on attention mechanism.Scientific reports · 2025Article
- Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping.Scientific reports · 2025Article
- Enhanced automated art curation using supervised modified CNN for art style classification.Scientific reports · 2025Article
- Enhanced glioma tumor detection and segmentation using modified deep learning with edge fusion and frequency features.Scientific reports · 2025Article
- A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images.Scientific reports · 2025Article
- Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks.Scientific reports · 2025Article
- VISION: View-specific integrated segmentation-classification framework for accurate brain tumor detection in MRI scans.PloS one · 2025Article
- Beyond visual inspection: the deep learning revolution in quantitative cerebrovascular imaging.Frontiers in neuroscience · 2025Review
- Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images.Scientific reports · 2024Article
- Efficient brain tumor grade classification using ensemble deep learning models.BMC medical imaging · 2024Article
- Accelerating Brain MR Imaging With Multisequence and Convolutional Neural Networks.Brain and behavior · 2024Article
- Clinical and CT characteristics for predicting lymph node metastasis in patients with synchronous multiple primary lung adenocarcinoma.BMC medical imaging · 2024Article
- Bio-inspired feature selection for early diagnosis of Parkinson's disease through optimization of deep 3D nested learning.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method's performance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.