The trainable segmentation model is based on two machine learning approaches or the image segmentation method. In this, it is possible to extract the nasopharyngeal carcinoma from the endoscopic images. After the learning phase, the neural network approach trains a classifier to complete the segmentation task.
A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences · Medicine, Engineering. Comput. Math.
This paper proposes a novel approach for diagnosing NPC from endoscopic images. The approach includes a trainable segmentation for identifying NPC tissues, ...
The use of medical images to diagnoses NPC tumor depends on tumor shape, region, and intensity. This paper proposes a novel approach for diagnosing NPC from ...
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Mohammed, Review on Nasopharyngeal Carcinoma: Concepts, methods of analysis, segmentation, classification, prediction and impact: a review of the research ...
May 17, 2024 · AttR2U-Net: A Fully Automated Model for MRI Nasopharyngeal Carcinoma Segmentation Based on Spatial Attention and Residual Recurrent Convolution.
Missing: Trainable | Show results with:Trainable
Jul 24, 2023 · We developed a deep learning-based NPC detection model using the you only look once (YOLO) network. Our model demonstrated high performance.
Apr 3, 2024 · ... model designed for fast segmentation of nasopharyngeal cancer images. ... Trainable Weka segmentation: a machine learning tool for ...
The study presents a novel automatic segmentation and identification for NPC by artificial neural networks from microscopy images without human intervention by ...
Oct 13, 2022 · To aid diagnosis, deep learning methods can provide interpretable clues for identifying NPC from magnetic resonance images (MRI).