Similar papers
Open access
Oct 2026
Deep learning-based automated brain tumor detection using a proposed convolutional neural network architecture
Brain tumors pose a critical threat to human health, often resulting in significant neurological impairments and high mortality rates. Early and accurate detection is essential for improving patient outcomes; however, conventional diagnostic methods, such as magnetic resonance imaging (MRI) interpretation by radiologis...
Ghaida Alsharef, R. Asiri, Lama Alsultan et al.
· Bulletin of Electrical Engin... · 0 citations
Benchmarking Deep Convolutional Neural Networks for Brain Tumor Detection Using Magnetic Resonance Imaging Data
This research systematically benchmarks five CNN architectures (VGG19, DenseNet201, ResNet50, Inception-v3, and MobileNet) on balanced and naturally imbalanced MRI datasets, suggesting that VGG19 is particularly good at discriminative performance.
Tegar Anugrah Firdaus, B. Rais, Marcelinus Jonathan Salim et al.
· 0 citations
Open access
2026
An enhanced convolutional neural network model for brain tumor classification using transfer learning and data augmentation
Brain tumors constitute a significant global health challenge, and accurate, timely diagnosis is critical for effective treatment planning. Manual interpretation of magnetic resonance imaging (MRI) remains time-consuming, error-prone and subject to inter-observer variability. The objective of this study was to develop...
Adukwu Nasiru Ukwuteno, G. Obunadike, B. T. Mashi
· Journal of Basics and Applie... · 0 citations
Review
Open access
Aug 2026
A Novel Explainable Deep Learning Model for Early-Stage Brain Tumor Classification: Multi-Level Feature Fusion from Merged MRI Datasets
A unique explainable deep learning model that integrates Tiny-ConvNeXt and DenseNet169 using multi-stage brain tumor classification is proposed, indicating that the suggested model provides a transparent and reliable framework for brain tumor detection, with potential applications in practical clinical decision support...
Md Sadi Al Huda, K. Tanvir, Zubaida Akhter et al.
· Artificial Intelligence and... · 0 citations
Open access
Aug 2026
Explainable Transfer Learning Framework for Multi-Class Brain Tumor Classification from MRI Images Using Comparative CNN Architectures
An explainable transfer-learning framework for four-class brain tumor classification (glioma, meningioma, pituitary tumor, and no-tumor) in which MobileNetV2, ResNet50, and EfficientNetB0 are compared under a common training protocol is presented.
Sif K. Ebis
· Journal of Al-Farabi for Eng... · 0 citations
Open access
Sep 2026
Hybrid Deep Learning Framework for Brain Tumor Classification Using MRI Images
The findings demonstrate the potential of combining complementary transfer-learning models with preprocessing, ensemble fusion, and explainable artificial intelligence for automated brain tumor classification from MRI images.
Ahmed Thijeel
· Alkadhim Journal for Compute... · 0 citations