ColonNet: A Hybrid Of DenseNet121 And U-NET Model For Detection And Segmentation Of GI Bleeding

• URL: http://arxiv.org/abs/2412.05216v1
• Date: Fri, 06 Dec 2024 17:48:06 GMT
• Title: ColonNet: A Hybrid Of DenseNet121 And U-NET Model For Detection And Segmentation Of GI Bleeding
• Authors: Ayushman Singh, Sharad Prakash, Aniket Das, Nidhi Kushwaha,
• Abstract summary: This study presents an integrated deep learning model for automatic detection and classification of Gastrointestinal bleeding in the frames extracted from Wireless Capsule Endoscopy (WCE) videos. The dataset has been released as part of Auto-WCBleedGen Challenge Version V2 hosted by the MISAHUB team.
• Score: 1.2499537119440245
• License:
• Abstract: This study presents an integrated deep learning model for automatic detection and classification of Gastrointestinal bleeding in the frames extracted from Wireless Capsule Endoscopy (WCE) videos. The dataset has been released as part of Auto-WCBleedGen Challenge Version V2 hosted by the MISAHUB team. Our model attained the highest performance among 75 teams that took part in this competition. It aims to efficiently utilizes CNN based model i.e. DenseNet and UNet to detect and segment bleeding and non-bleeding areas in the real-world complex dataset. The model achieves an impressive overall accuracy of 80% which would surely help a skilled doctor to carry out further diagnostics.

Related papers

• Transformer-Based Wireless Capsule Endoscopy Bleeding Tissue Detection and Classification [0.562479170374811]
  We design an end-to-end trainable model for the automatic detection and classification of bleeding and non-bleeding frames. Based on the DETR model, our model uses the Resnet50 for feature extraction, the transformer encoder-decoder for bleeding and non-bleeding region detection, and a feedforward neural network for classification. Trained in an end-to-end approach on the Auto-WCEBleedGen Version 1 challenge training set, our model performs both detection and classification tasks as a single unit.
  arXiv  Detail & Related papers  (2024-12-26T13:49:39Z)
• Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X [2.6374023322018916]
  This paper presents our solution to the Auto-WCEBleedGen Version V1 Challenge. We developed a unified YOLOv8-X model for both detection and classification of bleeding regions. Our approach achieved 96.10% classification accuracy and 76.8% mean Average Precision (mAP) at 0.5 IoU on the val idation dataset.
  arXiv  Detail & Related papers  (2024-12-21T13:37:11Z)
• SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms [60.35639972035727]
  The lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. Dice scores reached up to 0.838 $pm$ 0.066 and 0.716 $pm$ 0.125 on the respective datasets, with an average performance of up to 0.804 $pm$ 0.15.
  arXiv  Detail & Related papers  (2024-11-14T17:06:00Z)
• CapsuleNet: A Deep Learning Model To Classify GI Diseases Using EfficientNet-b7 [1.2499537119440245]
  We present CapsuleNet, a deep learning model developed for the Capsule Vision 2024 Challenge, aimed at classifying 10 distinct GI abnormalities. Our model leverages a pretrained EfficientNet-b7 backbone, tuned with additional layers for classification and optimized with PReLU activation functions. Our findings suggest that CNN-based models like CapsuleNet can provide an efficient solution for GI tract disease classification, particularly when inference time is a critical factor.
  arXiv  Detail & Related papers  (2024-10-24T20:43:47Z)
• Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images [0.9374652839580183]
  The aim is to automate the identification of ten GI abnormality classes, including angioectasia, bleeding, and ulcers. The proposed model achieves an overall accuracy of 94% across a well-structured dataset.
  arXiv  Detail & Related papers  (2024-10-24T06:10:31Z)
• Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development [59.74920439478643]
  In this paper, we collect and annotated the first benchmark dataset that covers diverse ERUS scenarios. Our ERUS-10K dataset comprises 77 videos and 10,000 high-resolution annotated frames. We introduce a benchmark model for colorectal cancer segmentation, named the Adaptive Sparse-context TRansformer (ASTR)
  arXiv  Detail & Related papers  (2024-08-19T15:04:42Z)
• A Meta-GNN approach to personalized seizure detection and classification [53.906130332172324]
  We propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We train a Meta-GNN based classifier that learns a global model from a set of training patients. We show that our method outperforms the baselines by reaching 82.7% on accuracy and 82.08% on F1 score after only 20 iterations on new unseen patients.
  arXiv  Detail & Related papers  (2022-11-01T14:12:58Z)
• Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation [52.77024349608834]
  This paper provides a way to analyze argyrophilic nucleolar organizer regions (AgNOR) stained slide using deep learning approaches. Our results show that the semantic segmentation using U-Net with ResNet-18 or ResNet-34 as the backbone have similar results. The best model shows an IoU for nucleus, cluster, and satellites of 0.83, 0.92, and 0.99 respectively.
  arXiv  Detail & Related papers  (2022-10-19T15:15:32Z)
• Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks [10.930181796935734]
  We present a deep learning framework for recognizing lesions in colonoscopy and capsule endoscopy images. To our knowledge, we present the first work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance.
  arXiv  Detail & Related papers  (2021-01-15T10:08:53Z)
• Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification [49.64863177155927]
  We propose a multi-task deep neural network with spatial activation mechanism to segment full retinal vessel, artery and vein simultaneously. The proposed network achieves pixel-wise accuracy of 95.70% for vessel segmentation, and A/V classification accuracy of 94.50%, which is the state-of-the-art performance for both tasks.
  arXiv  Detail & Related papers  (2020-07-18T05:46:47Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.