ArticleComputational intelligence and neuroscience2022
A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier.
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. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system.Scientific reports · 2026Article
- BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection.Brain informatics · 2026Article
- Optimized deep learning for brain tumor detection: a hybrid approach with attention mechanisms and clinical explainability.Scientific reports · 2025Article
- Hybrid of VGG-16 and FTVT-b16 Models to Enhance Brain Tumors Classification Using MRI Images.Diagnostics (Basel, Switzerland) · 2025Article
- Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data.Scientific reports · 2025Article
- Review
- CART-ANOVA-Based Transfer Learning Approach for Seven Distinct Tumor Classification Schemes with Generalization Capability.Diagnostics (Basel, Switzerland) · 2025Article
- AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.Journal of imaging informatics in medicine · 2025Article
- Article
- I-Brainer: Artificial intelligence/Internet of Things (AI/IoT)-Powered Detection of Brain CancerCurrent medical imaging · 2025Article
- A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor.Biomedical engineering and computational biology · 2024Article
- Microscopic parasite malaria classification using best feature selection based on generalized normal distribution optimization.PeerJ. Computer science · 2024Article
- Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach.BMC bioinformatics · 2023Article
- Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform.Scientific reports · 2023Article
- Ensemble deep learning for brain tumor detection.Frontiers in computational neuroscience · 2022Article
- An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification.Frontiers in public health · 2022Article
- MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction.Advances in neural information processing systems · 2022Article
- BTdiagAI: A Web-Deployed Hybrid Framework for Brain Tumor Classification Using Optimized MRI Preprocessing and Deep Learning Fusion.Healthcare technology lettersArticle
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningioma, no tumor, and pituitary tumor. The classified tumor images have been passed to the proposed Seg-network where the actual infected region is segmented to analyze the tumor severity level. The outcomes of the reported research have been evaluated on three benchmark datasets such as Kaggle, 2020-BRATS, and local collected images. The model achieved greater than 90% detection scores to prove the proposed model's effectiveness.
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