Monkeypox virus detection using pre-trained deep learning-based approaches

• URL: http://arxiv.org/abs/2209.04444v2
• Date: Sat, 17 Sep 2022 10:46:51 GMT
• Title: Monkeypox virus detection using pre-trained deep learning-based approaches
• Authors: Chiranjibi Sitaula, Tej Bahadur Shahi
• Abstract summary: Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. It is crucial to detect them earlier before widespread community transmission. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection.
• Score: 1.713291434132985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44\%, 85.47\%, 85.40\%, and 87.13\%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.

Related papers

• Bayesian Networks and Machine Learning for COVID-19 Severity Explanation and Demographic Symptom Classification [12.40025057417184]
  We present a three-stage data-driven approach to distill the hidden information about COVID-19. The first stage employs a Bayesian network structure learning method to identify the causal relationships among COVID-19 symptoms. As a second stage, the output serves as a useful guide to train an unsupervised machine learning (ML) algorithm. The final stage then leverages the labels obtained from clustering to train a demographic symptom identification model.
  arXiv  Detail & Related papers  (2024-06-16T05:43:24Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Symptom-based Machine Learning Models for the Early Detection of COVID-19: A Narrative Review [0.0]
  Machine learning models can analyze large datasets, incorporating patient-reported symptoms, clinical data, and medical imaging. In this paper, we provide an overview of the landscape of symptoms-only machine learning models for predicting COVID-19, including their performance and limitations. The review will also examine the performance of symptom-based models when compared to image-based models.
  arXiv  Detail & Related papers  (2023-12-08T01:41:42Z)
• Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification [60.49594822215981]
  This paper presents a classification model for detecting COVID-19 vaccination related search queries. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task.
  arXiv  Detail & Related papers  (2022-12-16T13:57:41Z)
• Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences [4.289396744209968]
  Influenza viruses mutate rapidly and can pose a threat to public health, especially to those in vulnerable groups. Recently, there has been increasing interest in using machine learning algorithms to provide fast and accurate predictions for viral sequences. In this study, real testing data sets and a variety of evaluation metrics were used to evaluate machine learning algorithms at different taxonomic levels.
  arXiv  Detail & Related papers  (2022-07-28T00:54:54Z)
• Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study [1.9395755884693817]
  Recent monkeypox outbreak has become a public health concern due to its rapid spread in more than 40 countries outside Africa. Computer-assisted detection of monkeypox lesions could be beneficial for surveillance and rapid identification of suspected cases. Deep learning methods have been found effective in the automated detection of skin lesions.
  arXiv  Detail & Related papers  (2022-07-06T09:09:28Z)
• Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16 [0.0]
  We introduce a newly developed "Monkeypox2022" dataset that is publicly available to use and can be obtained from our shared GitHub repository. We propose and evaluate a modified VGG16 model, which includes two distinct studies: Study One and Two. Our exploratory computational results indicate that our suggested model can identify Monkeypox patients with an accuracy of $97pm1.8%$.
  arXiv  Detail & Related papers  (2022-06-04T00:34:15Z)
• The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations [59.48245489413308]
  We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
  arXiv  Detail & Related papers  (2021-09-18T02:28:01Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)
• Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design [63.48989885374238]
  When the infection prevalence of a disease is low, Dorfman showed 80 years ago that testing groups of people can prove more efficient than testing people individually. Our goal in this paper is to propose new group testing algorithms that can operate in a noisy setting.
  arXiv  Detail & Related papers  (2020-04-26T23:41:33Z)

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.