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A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet

https://doi.org/10.3390/bioengineering9080368

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Watermarking Protocol Inspired Kidney Stone Segmentation in IoMT

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AI-based 3D Metrology and Defect Detection of HBMs in XRM Scans

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Application of an Improved U-Net-Based Edge Optimization Model in the Adjuvant Treatment of Bladder Cancer

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A novel MOSnet model for multi-object segmentation of medical images in micro-computed tomography

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Automatic Real-Time Detection and Diagnosis of Liver Tumor with Ultrasound

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Multi-Scale Fusion for Real-Time Image Observation and Data Analysis of Athletes after Soft Tissue Injury

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ResNet50-Boosted UNet for Improved Liver Segmentation Accuracy

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Deep Learning-Based Liver Histopathology Image Classification: State-of-the-Art Techniques and Emerging Trends

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Enhanced Liver Tumor Detection in CT Images Using 3D U-Net and Bat Algorithm for Hyperparameter Optimization

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inferred← citeshttps://doi.org/10.5114/pjr/210254openalex-snapshot

Chronic liver disease detection using deep convolutional neural networks with MRI data: a deep learning approach

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Deep Learning-Based Liver and Tumor Segmentation: A Comparative Analysis on the LiTS Dataset

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Digital Forensics Analysis of IoT Nodes using Machine Learning

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An Automatic Breast Cancer Diagnostic System Based on Mammographic Images Using Convolutional Neural Network Classifier

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Colorization-based U-Net Architecture for Precise Liver Tumor Segmentation in Clinical CT Images

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Research on 3D virtual vision matching based on interactive color segmentation

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