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.

The surface tension of blood plays an important role not only in the birth and decompression sickness but also in other functionality of the organism. It also provides capillary action during blood flow process. In this article, a simple and low-cost device is designed and fabricated for measuring the surface tension of blood by pendant drop method. In this device, a droplet of blood is formed in a closed chamber on tip of an 18-gauge blunt needle and it is photographed by a camera in very humid conditions (RH = 99%) to minimise the evaporation. A wetted wick is provided at the bottom of the chamber for maintaining constant relative humidity in chamber. Surface tension of blood is inferred using drop shape factor method and image analysis technique at various experimental conditions. This device is validated and calibrated with surface tension measurements of water and silicone oil. Its measurements are in good agreement against data reported in literature. Post-validation, surface tensions of blood samples with and without anticoagulant of healthy persons at various temperatures (range from 20 to 40 °C) was measured. It was found that the surface tension of normal blood samples strongly correlates with blood temperature. The surface tension of female blood was remarkably different from same of male blood. However, the effect of age (21–60 year) on the surface tension was negligible for all practical purposes. Increased percentage of anticoagulant in blood increases its surface tension. This research specifies a baseline for surface tension of normal blood samples at various conditions which in turn provides new insights to pathologists in identifying various disease conditions.