Search alternatives:
sars optimization » swarm optimization (Expand Search), art optimization (Expand Search), stress optimization (Expand Search)
robust detection » object detection (Expand Search), point detection (Expand Search), first detection (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based sars » based assays (Expand Search), based stress (Expand Search), based search (Expand Search)
sars optimization » swarm optimization (Expand Search), art optimization (Expand Search), stress optimization (Expand Search)
robust detection » object detection (Expand Search), point detection (Expand Search), first detection (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based sars » based assays (Expand Search), based stress (Expand Search), based search (Expand Search)
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Result comparison with other existing models.
Published 2025“…This study introduces a novel lung cancer detection method, which was mainly focused on Convolutional Neural Networks (CNN) and was later customized for binary and multiclass classification utilizing a publicly available dataset of chest CT scan images of lung cancer. …”
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Dataset distribution.
Published 2025“…This study introduces a novel lung cancer detection method, which was mainly focused on Convolutional Neural Networks (CNN) and was later customized for binary and multiclass classification utilizing a publicly available dataset of chest CT scan images of lung cancer. …”
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CNN structure for feature extraction.
Published 2025“…This study introduces a novel lung cancer detection method, which was mainly focused on Convolutional Neural Networks (CNN) and was later customized for binary and multiclass classification utilizing a publicly available dataset of chest CT scan images of lung cancer. …”
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Enhancing digital pathology workflows: computational blur detection for H&E image quality control in preclinical toxicology
Published 2025“…To address this, we have integrated a pair of productionalized computational models – ‘MiQC’ (Microscopic Quality Control) – into our routine image QC workflows. MiQC combines Local Binary Patterns (LBP) and DeepFocus-based deep learning algorithms to detect and quantify out-of-focus regions in WSIs. …”
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