Classification of COVID-19 X-ray Images Using a Combination of Deep and Handcrafted Features

• URL: http://arxiv.org/abs/2101.07866v2
• Date: Thu, 21 Jan 2021 17:21:01 GMT
• Title: Classification of COVID-19 X-ray Images Using a Combination of Deep and Handcrafted Features
• Authors: Weihan Zhang, Bryan Pogorelsky, Mark Loveland, Trevor Wolf
• Abstract summary: We use a combination of deep convolutional and handcrafted features extracted from X-ray chest scans to discriminate between healthy, common pneumonia, and COVID-19 patients. We achieve an accuracy of 0.988 in the classification task with our combined approach compared to 0.963 and 0.983 accuracy for the handcrafted features with SVM and CNN respectively.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Coronavirus Disease 2019 (COVID-19) demonstrated the need for accurate and fast diagnosis methods for emergent viral diseases. Soon after the emergence of COVID-19, medical practitioners used X-ray and computed tomography (CT) images of patients' lungs to detect COVID-19. Machine learning methods are capable of improving the identification accuracy of COVID-19 in X-ray and CT images, delivering near real-time results, while alleviating the burden on medical practitioners. In this work, we demonstrate the efficacy of a support vector machine (SVM) classifier, trained with a combination of deep convolutional and handcrafted features extracted from X-ray chest scans. We use this combination of features to discriminate between healthy, common pneumonia, and COVID-19 patients. The performance of the combined feature approach is compared with a standard convolutional neural network (CNN) and the SVM trained with handcrafted features. We find that combining the features in our novel framework improves the performance of the classification task compared to the independent application of convolutional and handcrafted features. Specifically, we achieve an accuracy of 0.988 in the classification task with our combined approach compared to 0.963 and 0.983 accuracy for the handcrafted features with SVM and CNN respectively.

Related papers

• Auto-Lesion Segmentation with a Novel Intensity Dark Channel Prior for COVID-19 Detection [0.24578723416255752]
  This study develops a CT-based radiomics framework for differentiation of COVID-19 from other lung diseases. The model categorizes images into three classes: COVID-19, non-COVID-19, or normal. The best performing classification model, Residual Neural Network with 50 layers (Resnet-50), attained an average accuracy, precision, recall, and F1-score of 98.8%, 99%, 98%, and 98% respectively.
  arXiv  Detail & Related papers  (2023-09-22T06:09:48Z)
• Detection of COVID19 in Chest X-Ray Images Using Transfer Learning [0.0]
  This paper investigates the concept of transfer learning using two of the most well-known VGGNet architectures, namely VGG-16 and VGG-19. We generated two different datasets to evaluate the performance of the proposed system for the identification of positive Covid-19 instances in a multiclass and binary classification problems.
  arXiv  Detail & Related papers  (2023-04-09T05:02:04Z)
• COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images [38.65823547986758]
  We propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection.
  arXiv  Detail & Related papers  (2022-12-19T07:10:51Z)
• Intelligent computational model for the classification of Covid-19 with chest radiography compared to other respiratory diseases [0.0]
  Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19.
  arXiv  Detail & Related papers  (2021-08-12T05:07:11Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection [61.04937460198252]
  We construct the X-ray imaging data from 2874 patients with four classes: normal, bacterial pneumonia, non-COVID-19 viral pneumonia, and COVID-19. To identify COVID-19, we propose a Focal Loss Based Neural Ensemble Network (FLANNEL) FLANNEL consistently outperforms baseline models on COVID-19 identification task in all metrics.
  arXiv  Detail & Related papers  (2020-10-30T03:17:31Z)
• PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images [1.4824891788575418]
  COVID-19 pandemic continues to severely undermine the prosperity of the global health system. The use of chest X-ray images for radiological assessment is one of the essential screening techniques. We propose a parallel-dilated convolutional neural network based COVID-19 detection system from chest x-ray images.
  arXiv  Detail & Related papers  (2020-07-29T12:28:16Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT [49.09507792800059]
  We propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. We evaluate our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP)
  arXiv  Detail & Related papers  (2020-05-07T06:00:02Z)
• 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)
• Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images [46.844349956057776]
  coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. There is still lack of studies on effectively quantifying the lung infection caused by COVID-19. We propose a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions.
  arXiv  Detail & Related papers  (2020-04-12T16:24:59Z)

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.