Machine learning approach for classification of maculopapular and vesicular rashes using the textural features of the skin images

Abstract

Skin, being the largest organ of the body, suffers different disorders, and one such is rashes caused because of infections. The rashes appear in different forms, and most often their texture features are different. The proposed algorithm classifies maculopapular and vesicular rashes of skin conditions using the machine learning approach. The initial pre-processing involved the segmentation of the rash region. The characteristics of the rashes were extracted from the skin images, and the Gray-Level Co-Occurrence Matrix (GLCM) method was incorporated for extracting the texture feature. The backpropagation neural model was trained with the rash images. The features extracted from the unsegmented and the segmented images were taken separately and trained and tested with the neural model. The performance of the model was studied for accuracy, sensitivity, specificity, and F1-score values. The developed machine learning algorithm has an average accuracy of 83.43% on the segmented images.

Keywords

• maculopapular
• vesicular
• glcm
• skin rash
• machine learning
• classification