Analysis and Identification of Dermatological Diseases Using Gaussian Mixture Modeling

• Main Authors: Chaahat Gupta, Naveen Kumar Gondhi, Parveen Kumar Lehana
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Artificial neural network; convolution neural network; dermatology; dermoscopy; Euclidean distance; Gaussian mixture model
• Online Access: https://ieeexplore.ieee.org/document/8766099/

Skin diseases are common and are mainly caused by virus, bacteria, fungus, or chemical disturbances. Timely analysis and identification are of utmost importance in order to control the further spread of these diseases. Control of these diseases is even more difficult in rural and resource-poor environments due to a lack of expertise in primary health centers. Hence, there is a need for providing self-assisting and innovative measures for the appropriate diagnosis of skin diseases. Use of mobile applications may provide inexpensive, simple, and efficient solutions for early diagnosis and treatment. This paper investigates the application of the Gaussian mixture model (GMM) based on the analysis and classification of skin diseases from their visual images using a Mahalanobis distance measure. The GMM has been preferred over the convolution neural network (CNN) because of limited resources available within the mobile device. Gray-level co-occurrence matrix (GLCM) parameters contrast, correlation, energy, and homogeneity derived from skin images have been used as the input data for the GMM. The analysis of the results showed that the proposed method is able to predict the classification of skin diseases with satisfactory efficiency. It was also observed that different diseases occupy distinct spatial positions in multidimensional space clustered using the Mahalanobis distance measure.