Benchmarking Deep Convolutional Neural Networks for Brain Tumor Detection Using Magnetic Resonance Imaging Data

An explainable fine-tuned deep learning network for precise brain tumor detection from MRI images

Brain Tumor Classification using Deep Residual Networks

Brain tumors represent one of the most critical and life-threatening forms of cancer worldwide, and accurate automated classification of MRI scans plays a crucial role in supporting timely diagnosis and treatment planning. In this study, a deep learning-based approach for automatic brain tumor classification is propose...

ResNet50 Versus VGG16 for MRI Brain Tumor Detection: A Transfer-Learning Study with Visual Explanation

Accurate detection of brain tumors from magnetic resonance imaging (MRI) is essential for early diagnosis and treatment planning. However, manual interpretation of MRI scans is time-consuming, requires experienced radiologists, and is subject to inter-observer variability. To address these challenges, this study propos...

Neural Gas Network-Based Distinction Between Brain Cysts and Tumors from Magnetic Resonance Images Using a Hybrid Deep Learning Algorithm.

The early and accurate diagnosis of brain tumors is critically important, as timely intervention significantly reduces mortality and improves patient outcomes. While magnetic resonance imaging (MRI) is the preferred diagnostic tool, differentiating malignant brain tumors from benign cysts remains a clinical challenge d...

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.

Brain Tumor Classification in MRI Images Using Convolutional Neural Networks with Explainable Artificial Intelligence

This study aims to enhance the transparency of Convolutional Neural Network (CNN)-based brain tumor classification models by implementing Explainable Artificial Intelligence (XAI) techniques, specifically Eigen-CAM and LIME, utilizing a dataset of 3,000 MRI images.