Extended Model for the Early Skin Cancer Detection Using Image Processing
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Authors
Poma, Jonathan Miguel CamposDominguez, Emily Yanira De La Cruz
Armas-Aguirre, Jimmy
Gonzalez, Leonor Gutierrez
Issue Date
2020-06-01
Metadata
Show full item recordPublisher
IEEE Computer SocietyJournal
Iberian Conference on Information Systems and Technologies, CISTIDOI
10.23919/CISTI49556.2020.9140952Additional Links
https://ieeexplore.ieee.org/document/9140952Abstract
In this research paper, we proposed an extended model for the early detection of skin cancer... The purpose is reduce the waiting time to obtaining a diagnosis, in addition, the function of the dermatoscope has been digitized by using a Smartphone and magnifying lenses as an accessory the mobile device. The proposed model has five phases: 1. The patient is attended by a general practitioner or nurse previously trained in any health center which has WiFi or mobile network connectivity to record their data and capture the skin lesion that will be analyzed. 2) The image will be in the cloud storage, which at the same time feeds an exclusive access website of dermatologists.3) Images are analyzed in real time using an image recognition service provided by IBM, which is integrated into a cloud-hosted web platform and an-Android application. 4)The result of the image processing is visualized by the dermatologist who makes a remote diagnosis.5) This diagnosis is received by the general practitioner or nurse, responsible for transmitting the diagnosis and treatment to the patient. This model was validated in a group of 60 patients, where 28 suffer from skin cancer in the early stage, 12 in the late stage and 20 are healthy patients, in a network of clinics in Lima, Peru. The obtained result was 97.5% of assertiveness on the analyzed skin lesions and 95% in healthy patients.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/embargoedAccessLanguage
engDescription
El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.ISSN
21660727EISSN
21660735ae974a485f413a2113503eed53cd6c53
10.23919/CISTI49556.2020.9140952
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