ArticleJournal of personalized medicine2023
Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks.
Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- CAE-BrainNet: a statistically validated class-adaptive attention ensemble model for explainable brain tumor classification from MRI.Brain informatics · 2026Article
- Hypertuned boosting approach with Local Binary Pattern and Pivot Distribution Count method feature extractor for glaucoma identification.Scientific reports · 2026Article
- Retrospection dropout bare-bones particle swarm optimization for feature-based brain tumor classification in MRI images.Frontiers in oncology · 2026Article
- A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation.PloS one · 2026Article
- Transfer learning for accurate brain tumor classification in MRI: a step forward in medical diagnostics.Discover oncology · 2025Article
- AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.Journal of imaging informatics in medicine · 2025Article
- A review of applications of automated ventricular parcellation from magnetic resonance imaging of the brain.Frontiers in neurology · 2025Review
- Brain Tumour Detection Using VGG-Based Feature Extraction With Modified DarkNet-53 Model.International journal of biomedical imaging · 2025Article
- Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images.Scientific reports · 2024Article
- Brain Tumor Detection and Classification Using an Optimized Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2024Article
- CMAF-Net: a cross-modal attention fusion-based deep neural network for incomplete multi-modal brain tumor segmentation.Quantitative imaging in medicine and surgery · 2024Article
- Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions.EJNMMI reports · 2024Review
- DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system.Scientific reports · 2024Article
- BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification.Scientific reports · 2024Article
- Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding.PloS one · 2024Article
- CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans.PeerJ. Computer science · 2024Article
- A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor.Biomedical engineering and computational biology · 2024Article
- A Robust Brain Tumor Detector Using BiLSTM and Mayfly Optimization and Multi-Level Thresholding.Biomedicines · 2023Article
- DenseNet_ HybWWoA: A DenseNet-Based Brain Metastasis Classification with a Hybrid Metaheuristic Feature Selection Strategy.Biomedicines · 2023Article
- ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
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Abstract
The field of medical image processing plays a significant role in brain tumor classification. The survival rate of patients can be increased by diagnosing the tumor at an early stage. Several automatic systems have been developed to perform the tumor recognition process. However, the existing systems could be more efficient in identifying the exact tumor region and hidden edge details with minimum computation complexity. The Harris Hawks optimized convolution network (HHOCNN) is used in this work to resolve these issues. The brain magnetic resonance (MR) images are pre-processed, and the noisy pixels are eliminated to minimize the false tumor recognition rate. Then, the candidate region process is applied to identify the tumor region. The candidate region method investigates the boundary regions with the help of the line segments concept, which reduces the loss of hidden edge details. Various features are extracted from the segmented region, which is classified by applying a convolutional neural network (CNN). The CNN computes the exact region of the tumor with fault tolerance. The proposed HHOCNN system was implemented using MATLAB, and performance was evaluated using pixel accuracy, error rate, accuracy, specificity, and sensitivity metrics. The nature-inspired Harris Hawks optimization algorithm minimizes the misclassification error rate and improves the overall tumor recognition accuracy to 98% achieved on the Kaggle dataset.
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