Yadav D P, Sharma Ashish, Singh Madhusudan, Goyal Ayush
1Department of Computer Engineering & ApplicationsGLA UniversityMathura281406India.
2School of Technology Studies, Endicott College of International StudiesWoosong UniversityDaejeon300-718South Korea.
IEEE J Transl Eng Health Med. 2019 Jul 18;7:1800507. doi: 10.1109/JTEHM.2019.2923628. eCollection 2019.
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 (MDS) approach.
烧伤是严重的公共卫生问题之一。通常，烧伤诊断基于医学专家和临床经验，需要医学或临床专家在康复诊所或医院急诊室进行检查。但有时患者可能在没有专业设施的地方烧伤，在这种情况下，计算机化自动烧伤评估工具可能有助于诊断。烧伤面积、深度和位置是确定烧伤严重程度的关键因素。本文提出了一种使用自动化机器学习诊断烧伤的分类模型。该研究的目的是开发用于烧伤分类的特征提取模型。基于支持向量机（SVM）的所提出方法在烧伤标准数据集——BIP_US数据库上进行评估。通过将图像分为两类进行训练，即需要植皮的和不需要植皮的。使用基于支持向量机的所提出方法对74张测试数据集图像进行测试，根据地面真值，基于支持向量机的模型准确率达到82.43%，高于过去使用多维缩放分析（MDS）方法所取得的79.73%。
