MELANOMA AUTOMATED DETECTION SYSTEM INTEGRATED WITH AN EHR PLATFORM

被引：0

作者：

Stefan, Ana - Maria ^{[1
]}

El-Khatib, Hassan ^{[2
]}

Popescu, Dan ^{[2
]}

机构：

[1] Natl Univ Sci & Technol POLITEHN, Dept Elect Telecommun & Informat Technol, Bucharest, Romania

[2] Natl Univ Sci & Technol POLITEHN, Dept Automat Control & Ind Informat, Bucharest, Romania

来源：

UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE | 2024年 / 86卷 / 01期

关键词：

electronic health record; automated melanoma detection system; classification model;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Early melanoma detection is vital for improved treatment outcomes and reduced mortality. This paper proposes integrating an automated melanoma detection system into the Electronic Health Record, offering benefits like seamless screening during routine medical visits, enhancing early lesion detection. The system's efficiency allows providers to focus on patient care, while real-time alerts ensure timely follow-up. Patient engagement promotes proactive skin health. Interoperability within the EHR facilitates comprehensive care, resulting in better outcomes, reduced costs, and proactive prevention.

引用

页码：57 / 68

页数：12

共 22 条

[1]

[Anonymous], ABOUT US

[2] Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification [J].

Carney, Michelle ;

Webster, Barron ;

Alvarado, Irene ;

Phillips, Kyle ;

Howell, Noura ;

Griffith, Jordan ;

Jongejan, Jonas ;

Pitaru, Amit ;

Chen, Alexander .

CHI'20: EXTENDED ABSTRACTS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, 2020,

[3]

Codella NCF, 2018, I S BIOMED IMAGING, P168, DOI 10.1109/ISBI.2018.8363547

[4] Dermatologist-level classification of skin cancer with deep neural networks [J].

Esteva, Andre ;

Kuprel, Brett ;

Novoa, Roberto A. ;

Ko, Justin ;

Swetter, Susan M. ;

Blau, Helen M. ;

Thrun, Sebastian .

NATURE, 2017, 542 (7639) :115-+

[5] Advances in Early Detection of Melanoma and the Future of At-Home Testing [J].

Garrison, Zachary R. ;

Hall, Connor M. ;

Fey, Rosalyn M. ;

Clister, Terri ;

Khan, Nabeela ;

Nichols, Rebecca ;

Kulkarni, Rajan P. .

LIFE-BASEL, 2023, 13 (04)

[6] Uveal melanoma incidence trends in Canada: a national comprehensive population-based study [J].

Ghazawi, Feras M. ;

Darwich, Rami ;

Le, Michelle ;

Rahme, Elham ;

Zubarev, Andrei ;

Moreau, Linda ;

Burnier, Julia Valdemarin ;

Sasseville, Denis ;

Burnier, Miguel N. ;

Litvinov, Ivan, V .

BRITISH JOURNAL OF OPHTHALMOLOGY, 2019, 103 (12) :1872-1876

[7] MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images [J].

Giotis, Ioannis ;

Molders, Nynke ;

Land, Sander ;

Biehl, Michael ;

Jonkman, Marcel F. ;

Petkov, Nicolai .

EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (19) :6578-6585

[8] Melanoma Treatment Costs A Systematic Review of the Literature, 1990-2011 [J].

Guy, Gery P., Jr. ;

Ekwueme, Donatus U. ;

Tangka, Florence K. ;

Richardson, Lisa C. .

AMERICAN JOURNAL OF PREVENTIVE MEDICINE, 2012, 43 (05) :537-545

[9]

Howard K., Hematology/Oncology Clinics of North America

[10] Computer aided Melanoma skin cancer detection using Image Processing [J].

Jain, Shivangi ;

Jagtap, Vandana ;

Pise, Nitin .

INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION AND CONVERGENCE (ICCC 2015), 2015, 48 :735-740

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