ArticleBioMed research international2021
Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 80 citations in OpenAlex.
- Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods.Journal of cancer research and clinical oncology · 2023Pooled it
- Comparative Analysis of Transfer Learning Based Residual Networks for the Brain Tumours Detection.Annals of neurosciences · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approaches.Scientific reports · 2025Article
- Enhanced glioma tumor detection and segmentation using modified deep learning with edge fusion and frequency features.Scientific reports · 2025Article
- Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks.Life (Basel, Switzerland) · 2025Article
- Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy.Diagnostics (Basel, Switzerland) · 2025Review
- IV3TM: Inception V3 enabled bidirectional long short-term memory network for brain tumor classification.PloS one · 2025Article
- Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.Bioengineering (Basel, Switzerland) · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Dual Deep CNN for Tumor Brain Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique.Diagnostics (Basel, Switzerland) · 2023Article
- GAM-SpCaNet: Gradient awareness minimization-based spinal convolution attention network for brain tumor classification.Journal of King Saud University. Computer and information sciences · 2023Article
- A Precise Medical Imaging Approach for Brain MRI Image Classification.Computational intelligence and neuroscience · 2022Article
- BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification.Computational intelligence and neuroscience · 2022Article
- ACU-Net: Attention-based convolutional U-Net model for segmenting brain tumors in fMRI images.Digital healthArticle
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumor is a fatal disease, caused by the growth of abnormal cells in the brain tissues. Therefore, early and accurate detection of this disease can save patient's life. This paper proposes a novel framework for the detection of brain tumor using magnetic resonance (MR) images. The framework is based on the fully convolutional neural network (FCNN) and transfer learning techniques. The proposed framework has five stages which are preprocessing, skull stripping, CNN-based tumor segmentation, postprocessing, and transfer learning-based brain tumor binary classification. In preprocessing, the MR images are filtered to eliminate the noise and are improve the contrast. For segmentation of brain tumor images, the proposed CNN architecture is used, and for postprocessing, the global threshold technique is utilized to eliminate small nontumor regions that enhanced segmentation results. In classification, GoogleNet model is employed on three publicly available datasets. The experimental results depict that the proposed method is achieved average accuracies of 96.50%, 97.50%, and 98% for segmentation and 96.49%, 97.31%, and 98.79% for classification of brain tumor on BRATS2018, BRATS2019, and BRATS2020 datasets, respectively. The outcomes demonstrate that the proposed framework is effective and efficient that attained high performance on BRATS2020 dataset than the other two datasets. According to the experimentation results, the proposed framework outperforms other recent studies in the literature. In addition, this research will uphold doctors and clinicians for automatic diagnosis of brain tumor disease.
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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.