A systematic evaluation reveals that EfficientNet-B0 achieves 91.30 percent average accuracy on real landfill waste imagery, though explainability analyses expose ...
Researchers used composite spectrograms and explainable machine learning to identify the sustained vowel sound as an acoustic marker for Parkinson's ...
Agricultural engineers have developed an automated computer vision framework that estimates piglet body weight from standard overhead video feeds by ...
Researchers demonstrated that deep convolutional neural networks, particularly VGG19, can classify five major Bangladeshi rice varieties with up to 99.73 ...
A combined ultrasound radiomics and deep learning model preoperatively predicted axillary lymph node metastatic burden in breast cancer patients.
A hybrid deep learning model combining sequential image enhancement and dynamic attention achieved 99.73 percent accuracy in classifying lung cancer ...
A systematic benchmarking study on a curated BRISC 2025 dataset demonstrated that deep model optimization and complete pretraining configurations strongly ...
A retrospective study demonstrates that pairing lightweight initial screening with selective expert escalation achieves high classification accuracy while substantially reducing ...
Researchers have engineered RaCAM, a self-contained Raspberry Pi camera system that automates microscopy imaging, specimen segmentation, and neural network fossil ...
A retrospective study demonstrates that a 2.5-dimensional Transformer fusion model using dynamic contrast-enhanced magnetic resonance imaging predicts breast cancer Ki-67 ...
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