ArticleComputational and structural biotechnology journal2022
Accurate brain tumor detection using deep convolutional neural network.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it, 352 citations in OpenAlex.
- Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- CNN Sample-Size Effects Across Biomedical Datasets: A Reliability Pattern in Overfitting, Ranking, and Monotonicity.Bioengineering (Basel, Switzerland) · 2026Article
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- Spinal-QDCNN: advanced feature extraction for brain tumor detection using MRI images.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Hyperparameter Optimization of Convolutional Neural Networks for Robust Tumor Image Classification.Diagnostics (Basel, Switzerland) · 2026Article
- MRI-based brain tumor prediction using convolutional neural network framework.Scientific reports · 2026Article
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- A contrastive learning framework with adaptive feature fusion for brain tumor classification.Scientific reports · 2026Article
- Automated Brain Tumor Classification and Grading Using Multi-scale Graph Neural Network with Spatio-Temporal Transformer Attention Through MRI Scans.Interdisciplinary sciences, computational life sciences · 2026Article
- BrainView: A cloud-based deep learning system for brain image segmentation, tumor detection and visualization.Biomedical journal · 2026Article
- An Optimized Strategy for Brain Tumor Classification Using SO(3) Equivariant Graph Neural Networks with Snow Geese Algorithm in MRI Imaging.Journal of molecular neuroscience : MN · 2026Article
- Article
- Boosting brain tumor detection with an optimized ResNet and explainability via Grad-CAM and LIME.Brain informatics · 2025Article
- I-BrainNet: Deep Learning and Internet of Things (DL/IoT)-Based Framework for the Classification of Brain Tumor.Journal of imaging informatics in medicine · 2025Article
- A novel deep neural architecture for efficient and scalable multidomain image classification.Scientific reports · 2025Article
- Hybrid Attention-Enhanced Xception and Dynamic Chaotic Whale Optimization for Brain Tumor Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms.Scientific reports · 2025Article
- A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans.Scientific reports · 2025Article
- Review
- Prediction of pathological grade of oral squamous cell carcinoma and construction of prognostic model based on deep learning algorithm.Discover oncology · 2025Article
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region. However, it is a time taking process and requires expertise to test the MRI images, manually. Nowadays, the advancement of Computer-assisted Diagnosis (CAD), machine learning, and deep learning in specific allow the radiologist to more reliably identify brain tumors. The traditional machine learning methods used to tackle this problem require a handcrafted feature for classification purposes. Whereas deep learning methods can be designed in a way to not require any handcrafted feature extraction while achieving accurate classification results. This paper proposes two deep learning models to identify both binary (normal and abnormal) and multiclass (meningioma, glioma, and pituitary) brain tumors. We use two publicly available datasets that include 3064 and 152 MRI images, respectively. To build our models, we first apply a 23-layers convolution neural network (CNN) to the first dataset since there is a large number of MRI images for the training purpose. However, when dealing with limited volumes of data, which is the case in the second dataset, our proposed "23-layers CNN" architecture faces overfitting problem. To address this issue, we use transfer learning and combine VGG16 architecture along with the reflection of our proposed "23 layers CNN" architecture. Finally, we compare our proposed models with those reported in the literature. Our experimental results indicate that our models achieve up to 97.8% and 100% classification accuracy for our employed datasets, respectively, exceeding all other state-of-the-art models. Our proposed models, employed datasets, and all the source codes are publicly available at: (https://github.com/saikat15010/Brain-Tumor-Detection).
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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.