MMUNet: Morphological feature enhancement network for colon cancer ...
2024年5月1日 · In this paper, we proposed a morphological method UNet (MMUNet) to solve irregular shapes and inaccurate edges of colon cancer segmentation in pathological images. We improved the base module of ConvNeXt [17] to make it suitable for colon cancer segmentation.
Segmentation and Grade Prediction of Colon Cancer Digital
2019年11月1日 · For cancer detection/grading, we encode cellular morphology, spatial architectural patterns of glands, and texture by extracting multi-scale features: (i) Gland-based: extracted from individual glands, (ii) local-patch-based: computed from randomly-selected image patches, and (iii) image-based: extracted from images, and employ a hierarchical ...
Segmentation and Grade Prediction of Colon Cancer Digital …
For cancer detection/grading, we encode cellular morphology, spatial architectural patterns of glands, and texture by extracting multi-scale features: (i) Gland-based: extracted from individual glands, (ii) local-patch-based: computed from randomly-selected image patches, and (iii) image-based: extracted from images, and employ a hierarchical ...
Transformer based on multi-scale local feature for colon cancer ...
In this paper, a transformer based on multi-scale local feature for colon cancer histopathological image classification model is proposed to realize the end-to-end classification of colon cancer histopathological images. The model employs FPS for patch sampling and utilizes ResNet to extract depth features from these patches.
Polyp detection in colonoscopy images using YOLOv11
2025年1月15日 · Colorectal cancer (CRC) is one of the most commonly diagnosed cancers all over the world. It starts as a polyp in the inner lining of the colon. To prevent CRC, early polyp detection is required. Colonosopy is used for the inspection of the colon. Generally, the images taken by the camera placed at the tip of the endoscope are analyzed by the experts manually. Various traditional machine ...
Deep learning for multi-class semantic segmentation enables colorectal …
2023年5月24日 · First, we introduce a deep learning based algorithm for multi-class semantic segmentation addressing fourteen different tissue types in whole-slide images of colorectal cancer, including not...
In this paper, the authors have proposed an algorithm for an automatic segmentation of glands in colon histology using local intensity and texture features. Here the dataset images are cropped into patches with different window sizes and taken the intensity of those patches, and also calculated texture-based features.
A novel deep learning-based algorithm combining …
2023年10月17日 · Therefore, we propose a novel deep learning-based algorithm combining tissue areas with histopathological features to predict cancer survival. The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset was used in this investigation.
Segmentation and Grade Prediction of Colon Cancer Digital
2019年11月1日 · For cancer detection/grading, we encode cellular morphology, spatial architectural patterns of glands, and texture by extracting multi-scale features: (i) Gland-based: extracted from individual glands, (ii) local-patch-based: computed from randomly-selected image patches, and (iii) image-based: extracted from images, and employ a hierarchical ...
Colon cancer stage detection in colonoscopy images using …
2023年2月1日 · Our research uses the single-stage YOLOv3 object detection model to locate and classify the various stages based on the TL value. It outputs the class probability and position coordinates of each image by enhancing accuracy and robustness.