ArticleScientific reports2024
Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 145 citations in OpenAlex.
- Comparative Analysis of Transfer Learning Based Residual Networks for the Brain Tumours Detection.Annals of neurosciences · 2026Article
- MRI-based brain tumor detection through an explainable EfficientNetV2 and MLP-mixer attention architecture.Physical and engineering sciences in medicine · 2026Article
- An advanced hybrid deep learning framework for high-precision brain tumor detection and classification in MRI scans.Scientific reports · 2026Article
- MRI-based brain tumor prediction using convolutional neural network framework.Scientific reports · 2026Article
- ResSGA-Net: A deep learning approach for enhanced brain tumor detection and accurate classification in healthcare imaging systems.Journal, genetic engineering & biotechnology · 2026Article
- A Robust ConvNeXt-Based Framework for Efficient, Generalizable, and Explainable Brain Tumor Classification on MRI.Bioengineering (Basel, Switzerland) · 2026Article
- Multi-class classification of brain tumors using optimized CNN and transfer learning techniques.Scientific reports · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN-Transformer Hybrids in Multimodal Imaging.International journal of biomedical imaging · 2026Review
- A scalable and reliable deep learning framework for enhanced brain tumor detection and diagnosis using AI-based medical imaging.Frontiers in medicine · 2026Article
- CMRA-DETR: a lightweight and high-accuracy detection framework for MRI-based brain tumor identification.Frontiers in medicine · 2026Article
- Fine-tuned ResNet34 for efficient brain tumor classification.Scientific reports · 2025Article
- Brain Tumor Classification in MRI Scans Using Edge Computing and a Shallow Attention-Guided CNN.Biomedicines · 2025Article
- Deep transfer learning approach for the classification of single and multiple power quality disturbances.Scientific reports · 2025Article
- Automatic Classification of Focal Liver Lesions Based on Multi-Sequence MRI.Journal of imaging informatics in medicine · 2025Article
- Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning.Scientific reports · 2025Article
- Advancing Spine Fracture Detection: The Role of Artificial Intelligence in Clinical Practice.Korean journal of neurotrauma · 2025Review
- Automatic smart brain tumor classification and prediction system using deep learning.Scientific reports · 2025Article
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health is very important for human life. In particular, the health of the brain, which is the executive of the vital resource, is very important. Diagnosis for human health is provided by magnetic resonance imaging (MRI) devices, which help health decision makers in critical organs such as brain health. Images from these devices are a source of big data for artificial intelligence. This big data enables high performance in image processing classification problems, which is a subfield of artificial intelligence. In this study, we aim to classify brain tumors such as glioma, meningioma, and pituitary tumor from brain MR images. Convolutional Neural Network (CNN) and CNN-based inception-V3, EfficientNetB4, VGG19, transfer learning methods were used for classification. F-score, recall, imprinting and accuracy were used to evaluate these models. The best accuracy result was obtained with VGG16 with 98%, while the F-score value of the same transfer learning model was 97%, the Area Under the Curve (AUC) value was 99%, the recall value was 98%, and the precision value was 98%. CNN architecture and CNN-based transfer learning models are very important for human health in early diagnosis and rapid treatment of such diseases.
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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.