Jun 7, 2024 · We aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett's esophagus and adenocarcinoma classification.
We aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett's esophagus and adenocarcinoma classification.
Hence, we aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett's esophagus and adenocarcinoma classification. For such a task, ...
We showed that local information and high-dimensional features are essential to improve the classification for our task. Besides, we observed a significant ...
Layer-selective deep representation to improve esophageal cancer classification ; Journal. Medical & Biological Engineering & Computing ; Published. Jun 7, 2024.
We showed that local information and high-dimensional features are essential to improve the classification for our task. Besides, we observed a significant ...
This research heralds a significant stride in the advancement of computer-aided endoscopic imaging for improved esophageal cancer diagnosis. Keywords: endoscopy ...
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Expert achieved 57.1% classification accuracy for artificial vs. real ulcer examination. The 5-layered CNN model achieved 94% classification accuracy on the ...
Oct 7, 2024 · This research paper aims to check the effectiveness of a variety of machine learning models in classifying esophageal cancer through MRI scans.
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This study proposes a binary class classification system for detecting EC subtypes in response to these challenges.
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