Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images

IEEE Journal of Translational Engineering in Health and Medicine
D P YadavAyush Goyal

Abstract

Burn is one of the serious public health problems. Usually, burn diagnoses are based on expert medical and clinical experience and it is necessary to have a medical or clinical expert to conduct an examination in restorative clinics or at emergency rooms in hospitals. But sometimes a patient may have a burn where there is no specialized facility available, and in such a case a computerized automatic burn assessment tool may aid diagnosis. Burn area, depth, and location are the critical factors in determining the severity of burns. In this paper, a classification model to diagnose burns is presented using automated machine learning. The objective of the research is to develop the feature extraction model to classify the burn. The proposed method based on support vector machine (SVM) is evaluated on a standard data set of burns-BIP_US database. Training is performed by classifying images into two classes, i.e., those that need grafts and those that are non-graft. The 74 images of test data set are tested with the proposed SVM based method and according to the ground truth, the accuracy of 82.43% was achieved for the SVM based model, which was higher than the 79.73% achieved in past work using the multidimensional scaling analysis...Continue Reading

Citations

Mar 16, 2021·Computers in Biology and Medicine·Brenda Rangel-Olvera, Roberto Rosas-Romero
Jul 9, 2021·Plastic and Reconstructive Surgery. Global Open·Angelos MantelakisAnkur Khajuria
Jun 15, 2021·Computers in Biology and Medicine·Behrouz RostamiZeyun Yu
Aug 21, 2021·Artificial Intelligence in Medicine·Bob Zhang, Jianhang Zhou
Aug 24, 2021·Burns and Trauma·Francisco Serra E MouraChidi Ekwobi
Sep 22, 2021·Advances in Wound Care·D M AnisuzzamanZeyun Yu

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Methods Mentioned

BETA
Feature Extraction
features extractions

Software Mentioned

MATLAB

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