Treffer: Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application.

Title:
Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application.
Authors:
Sahin VH; Software Engineering Department, Sakarya University, Sakarya, 54050, Turkey. vsahin@sakarya.edu.tr., Oztel I; Computer Engineering Department, Sakarya University, Sakarya, 54050, Turkey., Yolcu Oztel G; Software Engineering Department, Sakarya University, Sakarya, 54050, Turkey.
Source:
Journal of medical systems [J Med Syst] 2022 Oct 10; Vol. 46 (11), pp. 79. Date of Electronic Publication: 2022 Oct 10.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 7806056 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-689X (Electronic) Linking ISSN: 01485598 NLM ISO Abbreviation: J Med Syst Subsets: MEDLINE
Imprint Name(s):
Publication: 1999- : New York, NY : Kluwer Academic/Plenum Publishers
Original Publication: New York, Plenum Press.
MeSH Terms:
Deep Learning* , Mobile Applications* , Mpox, Monkeypox* , Skin Diseases*, Humans ; Neural Networks, Computer
References:
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Contributed Indexing:
Keywords: Android; Artificial Intelligence; Deep Learning; Mobile Application; Monkeypox; TensorFlow Lite
Entry Date(s):
Date Created: 20221009 Date Completed: 20221011 Latest Revision: 20250103
Update Code:
20250114
PubMed Central ID:
PMC9548428
DOI:
10.1007/s10916-022-01863-7
PMID:
36210365
Database:
MEDLINE

Weitere Informationen

Recently, human monkeypox outbreaks have been reported in many countries. According to the reports and studies, quick determination and isolation of infected people are essential to reduce the spread rate. This study presents an Android mobile application that uses deep learning to assist this situation. The application has been developed with Android Studio using Java programming language and Android SDK 12. Video images gathered through the mobile device's camera are dispatched to a deep convolutional neural network that runs on the same device. Camera2 API of the Android platform has been used for camera access and operations. The network then classifies images as positive or negative for monkeypox detection. The network's training has been carried out using skin lesion images of monkeypox-infected people and other skin lesion images. For this purpose, a publicly available dataset and a deep transfer learning approach have been used. All training and testing steps have been applied on Matlab using different pre-trained networks. Then, the network that has the best accuracy has been recreated and trained using TensorFlow. The TensorFlow model has been adapted to mobile devices by converting to the TensorFlow Lite model. The TensorFlow Lite model has been then embedded into the mobile application together with the TensorFlow Lite library for monkeypox detection. The application has been run on three devices successfully. During the run-time, the inference times have been gathered. 197 ms, 91 ms, and 138 ms average inference times have been observed. The presented system allows people with body lesions to quickly make a preliminary diagnosis. Thus, monkeypox-infected people can be encouraged to act rapidly to see an expert for a definitive diagnosis. According to the test results, the system can classify the images with 91.11% accuracy. In addition, the proposed mobile application can be trained for the preliminary diagnosis of other skin diseases.
(© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)