ArticleCancers2023
Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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Who cites it
13 citing papers in PubMed, 41 citations in OpenAlex.
- Deep Learning-Based Multi-Cancer Analysis for Predicting Disease-Free Survival Across Multiple Cancer Types.Cancers · 2026Article
- AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.Journal of imaging informatics in medicine · 2025Article
- Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.Journal of Cancer · 2025Review
- Brain Tumour Detection Using VGG-Based Feature Extraction With Modified DarkNet-53 Model.International journal of biomedical imaging · 2025Article
- MLG: a mixed local and global model for brain tumor classification.Frontiers in neuroscience · 2025Article
- Novel approach for Arabic fake news classification using embedding from large language features with CNN-LSTM ensemble model and explainable AI.Scientific reports · 2024Article
- Article
- Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach.BMC medical imaging · 2024Article
- Diagnosis of Forme Fruste Keratoconus Using Corvis ST Sequences with Digital Image Correlation and Machine Learning.Bioengineering (Basel, Switzerland) · 2024Article
- White blood cells classification using multi-fold pre-processing and optimized CNN model.Scientific reports · 2024Article
- Enhanced MRI-based brain tumour classification with a novel Pix2pix generative adversarial network augmentation framework.Brain communications · 2024Article
- Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques.PeerJ. Computer science · 2024Article
- A robust approach for multi-type classification of brain tumor using deep feature fusion.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumors and other nervous system cancers are among the top ten leading fatal diseases. The effective treatment of brain tumors depends on their early detection. This research work makes use of 13 features with a voting classifier that combines logistic regression with stochastic gradient descent using features extracted by deep convolutional layers for the efficient classification of tumorous victims from the normal. From the first and second-order brain tumor features, deep convolutional features are extracted for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classification. The proposed voting classifier along with convoluted features produces results that show the highest accuracy of 99.9%. Compared to cutting-edge methods, the proposed approach has demonstrated improved accuracy.
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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.