Detecting monkeypox in humans using deep learning

Muhammad Arslan Ijaz; Muhammad Kamran Abid; Naeem Aslam; Abdul Qadeer Mudaseer

Author(s)

Muhammad Arslan Ijaz Department of Computer Science, NFC Institute of Engineering and Technology,Multan, Punjab,Pakistan

Muhammad Kamran Abid Department of Computer Science, NFC Institute of Engineering and Technology,Multan, Punjab,Pakistan

Naeem Aslam Department of Computer Science, NFC Institute of Engineering and Technology,Multan, Punjab,Pakistan

Abdul Qadeer Mudaseer Department of Computer Science, NFC Institute of Engineering and Technology,Multan, Punjab,Pakistan

Abstract

The monkeypox virus is an orthopox virus that causes a contagious illness of the same name. The most visible symptom, along with fever, headache, and muscular pains, is a broad rash that develops into fluid-filled blisters. In the event of a monkeypox outbreak, swift response and efficient public health management depend on an early and accurate diagnosis. In this study, the feasibility of using deep keep learning techniques to diagnose monkeypox in humans is investigated. Long short-term memory (LSTM) networks are used to analyse time-series recordings of symptoms or patient data, whereas convolutional neural networks (CNNs) are used to process medical images of skin lesions. These models need to be trained on a large and reliable data set so that they can identify patterns and attributes that are specific to monkeypox.

Publication Details

Page(s) 265-272

DOI DOI not available

Published Journal: VFAST Transactions on Software Engineering, Volume: 11, Issue: 2, Year: 2023

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Keywords

Human, Deep learning, Detection, Monkeypox, Long Shot-Term Memory (LSTM), Convolutional Neural Networks (CNNs)