ArticleFrontiers in computational neuroscience2022
Ensemble deep learning for brain tumor detection.
Article in Frontiers in computational neuroscience, 2022. 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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19 citing papers in PubMed.
- Interpretable ensemble learning for tumor-type prediction with a SHAP-based evaluation of CatBoost and voting classifiers.Scientific reports · 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
- Machine Learning in MRI Brain Imaging: A Review of Methods, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Majority Voting Ensemble of Deep CNNs for Robust MRI-Based Brain Tumor Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.Scientific reports · 2025Article
- I-Brainer: Artificial intelligence/Internet of Things (AI/IoT)-Powered Detection of Brain CancerCurrent medical imaging · 2025Article
- Epileptic seizures diagnosis and prognosis from EEG signals using heterogeneous graph neural network.PeerJ. Computer science · 2025Article
- The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection.Journal of imaging · 2024Review
- Deep learning and transfer learning for brain tumor detection and classification.Biology methods & protocols · 2024Article
- A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor.Biomedical engineering and computational biology · 2024Article
- An Augmented Modulated Deep Learning Based Intelligent Predictive Model for Brain Tumor Detection Using GAN Ensemble.Sensors (Basel, Switzerland) · 2023Article
- Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning Aided Neuroimaging and Brain Regulation.Sensors (Basel, Switzerland) · 2023Review
- Saliency Map and Deep Learning in Binary Classification of Brain Tumours.Sensors (Basel, Switzerland) · 2023Article
- An Ensemble Model for the Diagnosis of Brain Tumors through MRIs.Diagnostics (Basel, Switzerland) · 2023Article
- Identifying Thematics in a Brain-Computer Interface Research.Computational intelligence and neuroscience · 2023Review
- Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images.Frontiers in oncology · 2023Article
- RDET stacking classifier: a novel machine learning based approach for stroke prediction using imbalance data.PeerJ. Computer science · 2023Article
- Millimeter-wave radar object classification using knowledge-assisted neural network.Frontiers in neuroscience · 2022Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.
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