Skin disease detection using Decision Tree and Support Vector Machine

Skin-related conditions are the most common health problems worldwide. Infections carry concealed risks that may lead to a spectrum of health challenges, encompassing both physical ailments and mental health disorders such as depression. In extreme instances, it has the potential to induce skin cancer. Manually diagnosing skin conditions by medical professionals is a laborious and subjective process. Thus, it is clear to patients and physicians that automatic skin disease prediction is necessary to enable quick treatment plans. The primary aim of this study is to predict whether a skin disease is benign or malignant. To reduce image noise, our approach utilizes Gaussian filtering. Subsequently, for each image contrast correlation energy and homogeneity was computed using GLCM- Gray Level Co-occurrence Matrix. Machine learning algorithms were applied for classification: Support Vector Machine (SVM) and Decision Tree (DT). We found that Decision Tree (DT) attained a classification accuracy of 91%, which is higher related to SVM.