Literature DB >> 15774281

A computer assisted diagnosis tool for the classification of burns by depth of injury.

Carmen Serrano1, Begoña Acha, Tomás Gómez-Cía, José I Acha, Laura M Roa.   

Abstract

In this paper, a computer assisted diagnosis (CAD) tool for the classification of burns into their depths is proposed. The aim of the system is to separate burn wounds from healthy skin, and to distinguish among the different types of burns (burn depths) by means of digital photographs. It is intended to be used as an aid to diagnosis in local medical centres, where there is a lack of specialists. Another potential use of the system is as an educational tool. The system is based on the analysis of digital photographs. It extracts from those images colour and texture information, as these are the characteristics observed by physicians in order to form a diagnosis. Clinical effectiveness of the method was demonstrated on 35 clinical burn wound images, yielding an average classification success rate of 88% compared to expert classified images.

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Year:  2005        PMID: 15774281     DOI: 10.1016/j.burns.2004.11.019

Source DB:  PubMed          Journal:  Burns        ISSN: 0305-4179            Impact factor:   2.744


  7 in total

1.  Artificial intelligence in the management and treatment of burns: a systematic review.

Authors:  Francisco Serra E Moura; Kavit Amin; Chidi Ekwobi
Journal:  Burns Trauma       Date:  2021-08-19

2.  Automated tissue classification framework for reproducible chronic wound assessment.

Authors:  Rashmi Mukherjee; Dhiraj Dhane Manohar; Dev Kumar Das; Arun Achar; Analava Mitra; Chandan Chakraborty
Journal:  Biomed Res Int       Date:  2014-07-08       Impact factor: 3.411

3.  A Smartphone App and Cloud-Based Consultation System for Burn Injury Emergency Care.

Authors:  Lee A Wallis; Julian Fleming; Marie Hasselberg; Lucie Laflamme; Johan Lundin
Journal:  PLoS One       Date:  2016-02-26       Impact factor: 3.240

4.  A Practical Approach to Artificial Intelligence in Plastic Surgery.

Authors:  Akash Chandawarkar; Christian Chartier; Jonathan Kanevsky; Phaedra E Cress
Journal:  Aesthet Surg J Open Forum       Date:  2020-01-08

5.  Deep Learning-Assisted Burn Wound Diagnosis: Diagnostic Model Development Study.

Authors:  Che Wei Chang; Feipei Lai; Mesakh Christian; Yu Chun Chen; Ching Hsu; Yo Shen Chen; Dun Hao Chang; Tyng Luen Roan; Yen Che Yu
Journal:  JMIR Med Inform       Date:  2021-12-02

6.  Clinically Inspired Skin Lesion Classification through the Detection of Dermoscopic Criteria for Basal Cell Carcinoma.

Authors:  Carmen Serrano; Manuel Lazo; Amalia Serrano; Tomás Toledo-Pastrana; Rubén Barros-Tornay; Begoña Acha
Journal:  J Imaging       Date:  2022-07-12

7.  Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery.

Authors:  Angelos Mantelakis; Yannis Assael; Parviz Sorooshian; Ankur Khajuria
Journal:  Plast Reconstr Surg Glob Open       Date:  2021-06-24
  7 in total

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