ArticleComputational intelligence and neuroscience2022
CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Cloud-Based Artificial Intelligence Classification of Common Intracranial Tumors on Magnetic Resonance Imaging.Cureus · 2026Article
- Nanomaterial-enhanced biosensors for traumatic brain injury biomarker detection: a review of analytical performance, machine learning integration, clinical validation, and point-of-care translation.Frontiers in neurology · 2026Review
- A novel deep neural architecture for efficient and scalable multidomain image classification.Scientific reports · 2025Article
- An effective flowchart for multimodal brain tumor binary classification with ranked 3D texture features.Scientific reports · 2025Article
- Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis.Scientific reports · 2025Article
- Review
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- Brain Tumour Detection Using VGG-Based Feature Extraction With Modified DarkNet-53 Model.International journal of biomedical imaging · 2025Article
- Development of brain tumor radiogenomic classification using GAN-based augmentation of MRI slices in the newly released gazi brains dataset.BMC medical informatics and decision making · 2024Article
- Advancing brain tumor detection: harnessing the Swin Transformer's power for accurate classification and performance analysis.PeerJ. Computer science · 2024Article
- Article
- Secure medical image transmission using deep neural network in e-health applications.Healthcare technology letters · 2023Article
- Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges.Bioengineering (Basel, Switzerland) · 2023Article
- A Robust Brain Tumor Detector Using BiLSTM and Mayfly Optimization and Multi-Level Thresholding.Biomedicines · 2023Article
- Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique.Diagnostics (Basel, Switzerland) · 2023Article
- Classification of Brain Tumor Images Using CNN.Computational intelligence and neuroscience · 2023Article
- A Survey on Deep Learning in COVID-19 Diagnosis.Journal of imaging · 2022Review
- Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model.Diagnostics (Basel, Switzerland) · 2022Article
- Ensemble deep learning for brain tumor detection.Frontiers in computational neuroscience · 2022Article
Corrections and comments
- Retracted
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.
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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.