A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans

• URL: http://arxiv.org/abs/2208.02910v1
• Date: Thu, 4 Aug 2022 22:14:18 GMT
• Title: A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans
• Authors: Shiyi Wang, Guang Yang
• Abstract summary: In COVID-19 computed tomography (CT) images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis. Some researchers propose the relevant DL models which can replace professional diagnostic specialists in clinics when lacking expertise. Our model achieves 94.52% accuracy in the classification of lung lesions by 3 types: COVID, Pneumonia and Normal.
• Score: 5.5957919486531935
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation. In COVID-19 computed tomography (CT) images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis. Based on this situation, some researchers propose the relevant DL models which can replace professional diagnostic specialists in clinics when lacking expertise. However, although DL methods have a stunning performance in medical image processing, the limited datasets can be a challenge in developing the accuracy of diagnosis at the human level. In addition, deep learning algorithms face the challenge of classifying and segmenting medical images in three or even multiple dimensions and maintaining high accuracy rates. Consequently, with a guaranteed high level of accuracy, our model can classify the patients' CT images into three types: Normal, Pneumonia and COVID. Subsequently, two datasets are used for segmentation, one of the datasets even has only a limited amount of data (20 cases). Our system combined the classification model and the segmentation model together, a fully integrated diagnostic model was built on the basis of ResNet50 and 3D U-Net algorithm. By feeding with different datasets, the COVID image segmentation of the infected area will be carried out according to classification results. Our model achieves 94.52% accuracy in the classification of lung lesions by 3 types: COVID, Pneumonia and Normal. For future medical use, embedding the model into the medical facilities might be an efficient way of assisting or substituting doctors with diagnoses, therefore, a broader range of the problem of variant viruses in the COVID-19 situation may also be successfully solved.

Related papers

• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• PrepNet: A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis [0.22485007639406518]
  COVID-19 diagnosis can now be done efficiently using PCR tests, but this use case exemplifies the need for a methodology to overcome data variability issues. We propose a novel generative approach that aims at erasing the differences induced by e.g. the imaging technology while simultaneously introducing minimal changes to the CT scans.
  arXiv  Detail & Related papers  (2022-08-19T15:49:47Z)
• Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image [3.135883872525168]
  Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff. Deep learning has been applied to perform COVID-19 infection region segmentation and disease classification. We propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration.
  arXiv  Detail & Related papers  (2022-05-27T20:06:45Z)
• Interpretable Automated Diagnosis of Retinal Disease using Deep OCT Analysis [7.005458308454871]
  We develop a CNN-based model for accurate classification of OCT scans. We place an emphasis on producing both qualitative and quantitative explanations of the model's decisions. Our work is the first to produce detailed explanations of the model's decisions.
  arXiv  Detail & Related papers  (2021-09-03T17:59:34Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing [4.446085353384894]
  COVID-19 is a global pandemic disease overgrowing worldwide. Computer-aided screening tools with greater sensitivity is imperative for disease diagnosis and prognosis. This article proposes a 3D Convolutional Neural Network (CNN)-based classification approach.
  arXiv  Detail & Related papers  (2021-02-11T18:16:18Z)
• Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans [72.04652116817238]
  We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification. We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
  arXiv  Detail & Related papers  (2021-01-14T03:45:01Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)
• Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net [0.0]
  The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. We propose an innovative automated segmentation pipeline for COVID-19 infected regions. Our method focuses on on-the-fly generation of unique and random image patches for training by performing several preprocessing methods.
  arXiv  Detail & Related papers  (2020-06-24T17:29:26Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning [48.05232274463484]
  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
  arXiv  Detail & Related papers  (2020-05-06T15:19:15Z)

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.