ArticleHealth information science and systems2023
BTC-fCNN: Fast Convolution Neural Network for Multi-class Brain Tumor Classification.
Article in Health information science and systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Cloud-Based Artificial Intelligence Classification of Common Intracranial Tumors on Magnetic Resonance Imaging.Cureus · 2026Article
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- A novel rapid non-contact detection method for frozen and thawed foods : Based on bio-impedance spectroscopy analysis and neural network (FCNN) recognition model.Current research in food science · 2026Article
- A novel adaptive sigma KNN model for depression and anxiety detection following the COVID 19 pandemic.Scientific reports · 2025Article
- A deep ensemble learning framework for brain tumor classification using data balancing and fine-tuning.Scientific reports · 2025Article
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- CLIF-Net: Intersection-guided Cross-view Fusion Network for Infection Detection from Cranial Ultrasound.medRxiv : the preprint server for health sciences · 2025Article
- Automatic smart brain tumor classification and prediction system using deep learning.Scientific reports · 2025Article
- Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks.Life (Basel, Switzerland) · 2025Article
- AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.Journal of imaging informatics in medicine · 2025Article
- Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning.Health information science and systems · 2024Article
- Enhancing brain tumor classification through ensemble attention mechanism.Scientific reports · 2024Article
- COVID-19 Detection and Diagnosis Model on CT Scans Based on AI Techniques.Bioengineering (Basel, Switzerland) · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis.Journal of personalized medicine · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Timely prognosis of brain tumors has a crucial role for powerful healthcare of remedy-making plans. Manual classification of the brain tumors in magnetic resonance imaging (MRI) images is a challenging task, which relies on the experienced radiologists to identify and classify the brain tumor. Automated classification of different brain tumors is significant based on designing computer-aided diagnosis (CAD) systems. Existing classification methods suffer from unsatisfactory performance and/or large computational cost/ time. This paper proposed a fast and efficient classification process, called BTC-fCNN, which is a deep learning-based system to distinguish between different views of three brain tumor types, namely meningioma, glioma, and pituitary tumors. The proposed system's model was applied on MRI images from the Figshare dataset. It consists of 13 layers with few trainable parameters involving convolution layer, 1 × 1 convolution layer, average pooling, fully connected layer, and softmax layer. Five iterations including transfer learning and five-fold cross-validation for retraining are considered to increase the proposed model performance. The proposed model achieved 98.63% average accuracy, using five iterations with transfer learning, and 98.86% using retrained five-fold cross-validation (internal transfer learning between the folds). Various evaluation metrics were measured to evaluate the proposed model, such as precision, F-score, recall, specificity and confusion matrix. The proposed BTC-fCNN model outstrips the state-of-the-art and other well-known convolution neural networks (CNN).
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