ArticleScientific reports2025
Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Multi-Chaotic HEOA for Hardware-Aware Neural Architecture Search: Brain Tumor Classification on FPGA.Sensors (Basel, Switzerland) · 2026Article
- Are AI Neuroimaging Models Ready for Clinical Use? A Systematic Methodological Review.Journal of clinical medicine · 2026Review
- An advanced hybrid deep learning framework for high-precision brain tumor detection and classification in MRI scans.Scientific reports · 2026Article
- Liver cancer segmentator: Metadata-guided confidence scoring for reliable segmentation of colorectal liver metastases in CT.Computer methods and programs in biomedicine · 2026Article
- Alzheimer's disease prediction algorithm based on hippocampal longitudinal hybrid morphological features.Quantitative imaging in medicine and surgery · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Breast tumor segmentation and morphological feature-based classification in ultrasound using a two-stage U-net and SVM.Frontiers in bioengineering and biotechnology · 2026Article
- Enhanced Feature Extraction for Detection and Classification of Kidney Abnormalities.Current medical imaging · 2026Article
- A dual-branch network for lesion segmentation in medical images using state space models.Quantitative imaging in medicine and surgery · 2025Article
- Development of a hybrid 2.5D deep learning model for glioma survival prediction using T1-weighted MRI from the CGGA database.European journal of radiology open · 2025Article
- PSF-GRBM: brain tumor classification and grading using optimized gated recurrent unit-deep bidirectional long short-term memory.Journal of neuro-oncology · 2025Article
- FALS-YOLO: An Efficient and Lightweight Method for Automatic Brain Tumor Detection and Segmentation.Sensors (Basel, Switzerland) · 2025Article
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives.Journal of imaging · 2025Review
- Towards precision oncology: a multi-level cancer classification system integrating liquid biopsy and machine learning.BioData mining · 2025Article
- Article
- An advanced multimodal image fusion model for accurate detection of Alzheimer's disease using MRI and PET.Frontiers in medical technology · 2025Article
- Editorial: Current challenges and future perspectives in neuro-oncological imaging.Frontiers in radiology · 2025Article
- Applications of machine learning for computer-aided diagnosis of Parkinson's disease: progress and benchmark case study.Artificial intelligence review · 2025Article
- Quality and reliability of Alzheimer's disease videos on Douyin and Bilibili: A cross-sectional content analysis study.Digital healthArticle
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8 authors.
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Abstract
The most prevalent form of malignant tumors that originate in the brain are known as gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and resilient segmentation of the tumors, along with an estimation of the patients' overall survival rate. Therefore, we have introduced a deep learning approach that employs a combination of MRI scans to accurately segment brain tumors and predict survival in patients with gliomas. To ensure strong and reliable tumor segmentation, we employ 2D volumetric convolution neural network architectures that utilize a majority rule. This method helps to significantly decrease model bias and improve performance. Additionally, in order to predict survival rates, we extract radiomic features from the tumor regions that have been segmented, and then use a Deep Learning Inspired 3D replicator neural network to identify the most effective features. The model presented in this study was successful in segmenting brain tumors and predicting the outcome of enhancing tumor and real enhancing tumor. The model was evaluated using the BRATS2020 benchmarks dataset, and the obtained results are quite satisfactory and promising.
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