TL;DR
A Systematic deep learning framework based on an enhanced ResNet50 architecture to improve brain tumor classification and surpass benchmark models such as VGG16, MobileNet, InceptionV3, and Xception is introduced.
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.
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...
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...
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.
This study presents innovative approaches for diagnosing and classifying brain tumors from MRI images using advanced deep learning models to address clinical data constraints — including single-center acquisition, slice-level (not pixel-level) labeling, and real-world imaging variability — alongside extreme class...
Results from various performance evaluation metrics indicate that the proposed fusion-fusion-based DL model (FusionNetX) can accurately detect and predict brain tumors and help health practitioners make timely decisions.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
