AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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.
Improvements in detection accuracy and interpretability reveal a critical trade-off: while macro-accuracy reaches 97.1%, Metastatic tumor recall remains at 75% — underscoring that clinical AI must prioritize equitable error distribution over aggregate metrics.
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.
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