TL;DR
This study introduces XIM+CMAES, a novel ensemble deep learning framework that integrates four state-of-the-art transfer learning models—Xception, InceptionV3, MobileNet, and MobileNetV2—whose outputs are adaptively fused using the Covariance Matrix Adaptation Evolution Strategy (CMAES).
Reliable brain-tumor diagnosis from magnetic resonance imaging (MRI) requires more than high classification accuracy. Deep neural networks can be overconfident under scanner, protocol, demographic, and disease-spectrum shifts, while visually plausible explanations may not faithfully identify the evidence driving a pred...
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...
The proposed NeuroTrustNet is an integrated multimodal framework that combines complementary convolutional neural network, Vision Transformer, and handcrafted radiomic representations through the proposed Adaptive Attention Stacking mechanism, enabling sample-specific feature fusion to improve robustness under cross-da...
These findings suggest that interpretability quality can be quantitatively assessed and improved through multi-method and ensemble-based analysis and enhances explanation robustness and supports reliable and transparent clinical decision support for prostate magnetic resonance imaging.
Accurate brain tumor detection is essential for effective clinical diagnosis; however, the performance of deep learning models is highly sensitive to manually selected architectures and hyperparameters. To address this challenge, this paper presents a reinforcement learning–inspired automated optimization framework for...
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